The goal is to build a pneumonia detection system, to locate the position of inflammation in an image.
Tissues with sparse material, such as lungs which are full of air, do not absorb the X-rays and appear black in the image. Dense tissues such as bones absorb X-rays and appear white in the image. While we are theoretically detecting “lung opacities”, there are lung opacities that are not pneumonia related.
In the data, some of these are labeled “Not Normal No Lung Opacity”. This extra third class indicates that while pneumonia was determined not to be present, there was nonetheless some type of abnormality on the image and oftentimes this finding may mimic the appearance of true pneumonia.
Dicom original images: - Medical images are stored in a special format called DICOM files (*.dcm). They contain a combination of header metadata as well as underlying raw image arrays for pixel data
import pandas as pd
import numpy as np
from sklearn import metrics
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from google.colab import drive
drive.mount('/content/drive')
Project_Path = '/content/drive/My Drive/AIML - Projects/Capstone Project - CV1/'
Project_data_Path = '/content/drive/My Drive/AIML - Projects/Capstone Project - CV1/rsna-pneumonia-detection-challenge/'
Project_Train_Images_Path = Project_data_Path + 'stage_2_train_images/'
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
!cd /content
!pwd
!ls -lrt
!unzip "/content/drive/My Drive/AIML - Projects/Capstone Project - CV1/rsna-pneumonia-detection-challenge.zip" -d "/content/CapstoneProject" > /dev/null
Streaming output truncated to the last 5000 lines.
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## Set the project path to the local Google Colab directory
Project_Path = '/content/CapstoneProject/'
Project_data_Path = '/content/CapstoneProject/rsna-pneumonia-detection-challenge/'
Project_Train_Images_Path = Project_data_Path + 'stage_2_train_images/'
## Read the csv files in data frames
datainfo = pd.read_csv(Project_data_Path + "stage_2_detailed_class_info.csv")
datalabels = pd.read_csv(Project_data_Path + "stage_2_train_labels.csv")
## Explore the data in the labels file
print (datalabels.head(5))
print (datalabels.tail(5))
patientId x y width height Target
0 0004cfab-14fd-4e49-80ba-63a80b6bddd6 NaN NaN NaN NaN 0
1 00313ee0-9eaa-42f4-b0ab-c148ed3241cd NaN NaN NaN NaN 0
2 00322d4d-1c29-4943-afc9-b6754be640eb NaN NaN NaN NaN 0
3 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 NaN NaN NaN NaN 0
4 00436515-870c-4b36-a041-de91049b9ab4 264.0 152.0 213.0 379.0 1
patientId x y width height \
30222 c1ec14ff-f6d7-4b38-b0cb-fe07041cbdc8 185.0 298.0 228.0 379.0
30223 c1edf42b-5958-47ff-a1e7-4f23d99583ba NaN NaN NaN NaN
30224 c1f6b555-2eb1-4231-98f6-50a963976431 NaN NaN NaN NaN
30225 c1f7889a-9ea9-4acb-b64c-b737c929599a 570.0 393.0 261.0 345.0
30226 c1f7889a-9ea9-4acb-b64c-b737c929599a 233.0 424.0 201.0 356.0
Target
30222 1
30223 0
30224 0
30225 1
30226 1
print (datalabels.shape)
(30227, 6)
## Let's check the balance of target variable
print (datalabels['Target'].value_counts())
0 20672 1 9555 Name: Target, dtype: int64
## Let's check nulls in the dataframe
print (datalabels.isna().sum())
patientId 0 x 20672 y 20672 width 20672 height 20672 Target 0 dtype: int64
There are no nulls in Target and patientId columns.
Columns x,y, width and height are having nulls for 20672 rows.
Target column distribution: 20672 are pneumonia negative and 9555 are pneumonia positive. This is good proportion.
from collections import Counter
DupPatients = datalabels.duplicated()
print ("Duplicate patients: ", DupPatients.value_counts())
Duplicate patients: False 30227 dtype: int64
dfmultirowpatients = datalabels.groupby('patientId').filter(lambda x: len(x) > 2)
dfmultirowpatients.head
<bound method NDFrame.head of patientId x y width height \
122 03a9498c-549d-4e7d-800b-e74797f7f625 195.0 511.0 218.0 271.0
123 03a9498c-549d-4e7d-800b-e74797f7f625 612.0 214.0 109.0 89.0
124 03a9498c-549d-4e7d-800b-e74797f7f625 610.0 599.0 265.0 245.0
144 040a0743-f663-4746-8224-f0e3bacc7ba5 294.0 228.0 87.0 144.0
145 040a0743-f663-4746-8224-f0e3bacc7ba5 180.0 504.0 236.0 205.0
... ... ... ... ... ...
30007 2cd0cbc4-bf35-49de-963e-8ee3fd08ca75 630.0 389.0 195.0 95.0
30008 2cd0cbc4-bf35-49de-963e-8ee3fd08ca75 291.0 224.0 153.0 119.0
30085 2f686840-18b3-45ac-a2bb-6140e3b0ac73 756.0 581.0 211.0 143.0
30086 2f686840-18b3-45ac-a2bb-6140e3b0ac73 358.0 547.0 139.0 126.0
30087 2f686840-18b3-45ac-a2bb-6140e3b0ac73 751.0 316.0 154.0 177.0
Target
122 1
123 1
124 1
144 1
145 1
... ...
30007 1
30008 1
30085 1
30086 1
30087 1
[409 rows x 6 columns]>
print ("No of patients with multiple rows:", dfmultirowpatients.size)
No of patients with multiple rows: 2454
dfmultirowpatients['patientId'].value_counts()
7d674c82-5501-4730-92c5-d241fd6911e7 4
1c44e0a4-4612-438f-9a83-8d5bf919cb67 4
31764d54-ea3b-434f-bae2-8c579ed13799 4
ee820aa5-4804-4984-97b3-f0a71d69702f 4
0e03c2d7-9cea-4dc7-8e91-794a8b3ff58d 4
..
3d895c52-0627-42b3-ba67-41a08f387da0 3
3c1fdc6b-2e9a-46f7-8dbd-28d4155f9b93 3
3a510377-9cb0-4fa2-97fd-3c663e64ec9f 3
36d7ebd2-a4e5-42d1-bde0-c7699da0b152 3
2f686840-18b3-45ac-a2bb-6140e3b0ac73 3
Name: patientId, Length: 132, dtype: int64
print ("No of patients with multiple anomalies in the X=ray: ",dfmultirowpatients['patientId'].nunique())
No of patients with multiple anomalies in the X=ray: 132
Total 132 patients are having more than 1 anomalies.
Total 2454 rows in the csv in all represent num of patches with multiple anomalies.
##Let's display the distribution of the target variable - 'Class' from datainfo dataframe
label_count=datainfo['class'].value_counts()
explode = (0.01,0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('Class Distribution')
plt.show()
Train_df=datainfo.merge(datalabels, left_on='patientId', right_on='patientId', how='inner' )
Train_df.sample(5)
| patientId | class | x | y | width | height | Target | |
|---|---|---|---|---|---|---|---|
| 33039 | ec903e13-0397-46d8-8b5b-194631e31bf6 | No Lung Opacity / Not Normal | NaN | NaN | NaN | NaN | 0 |
| 4414 | 366c5768-5fba-4ba1-8532-082d3ba3c9bf | Lung Opacity | 227.0 | 577.0 | 176.0 | 169.0 | 1 |
| 6946 | 429491d5-4da9-4342-aeea-4f5c38f3de3d | No Lung Opacity / Not Normal | NaN | NaN | NaN | NaN | 0 |
| 3906 | 3454c720-b91a-4605-99cb-b0e207aa4df7 | Normal | NaN | NaN | NaN | NaN | 0 |
| 22752 | ad587880-a108-4b52-bf4a-28d248bb1539 | Lung Opacity | 224.0 | 405.0 | 135.0 | 315.0 | 1 |
fig, ax = plt.subplots(nrows=1,figsize=(6,6))
temp_data = Train_df.groupby('Target')['class'].value_counts()
df = pd.DataFrame(data={'Exams': temp_data.values}, index=temp_data.index).reset_index()
sns.barplot(ax=ax, x = 'Target', y = 'Exams', hue='class', data = df)
plt.title("Chest exams class and Target")
plt.show()
## Let's load the patients related data from the dcm files in the dataframe.
%pip install pydicom
import pydicom as dicom
from tqdm import tqdm
patient_info_df = pd.DataFrame(columns=['age','sex','patientId'])
for ix,id_ in tqdm(enumerate(datainfo['patientId'])):
#age=dicom.read_file(os.path.join(Project_Train_Images_Path,
# id_+'.dcm')).PatientAge
age=dicom.read_file(Project_Train_Images_Path + id_ + '.dcm').PatientAge
sex=dicom.read_file(Project_Train_Images_Path + id_ + '.dcm').PatientSex
patient_info_df.loc[ix,'age']=age
patient_info_df.loc[ix,'sex']=sex
patient_info_df.loc[ix,'patientId'] = id_
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: pydicom in /usr/local/lib/python3.7/dist-packages (2.3.0)
30227it [03:42, 135.59it/s]
patient_info_df.head(10)
patient_info_df_orig = patient_info_df.copy()
##Merging this patient information with the exiting meta data
patient_info_df = pd.merge(datainfo,patient_info_df,on="patientId")
patient_info_df
| patientId | class | age | sex | |
|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | No Lung Opacity / Not Normal | 51 | F |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | No Lung Opacity / Not Normal | 48 | F |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | No Lung Opacity / Not Normal | 19 | M |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | Normal | 28 | M |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | Lung Opacity | 32 | F |
| ... | ... | ... | ... | ... |
| 37624 | c1f6b555-2eb1-4231-98f6-50a963976431 | Normal | 45 | M |
| 37625 | c1f7889a-9ea9-4acb-b64c-b737c929599a | Lung Opacity | 72 | F |
| 37626 | c1f7889a-9ea9-4acb-b64c-b737c929599a | Lung Opacity | 72 | F |
| 37627 | c1f7889a-9ea9-4acb-b64c-b737c929599a | Lung Opacity | 72 | F |
| 37628 | c1f7889a-9ea9-4acb-b64c-b737c929599a | Lung Opacity | 72 | F |
37629 rows × 4 columns
## Merge the labels data into the info data
merged_data_info = pd.merge(datalabels,patient_info_df,on="patientId")
merged_data_info.tail(10)
| patientId | x | y | width | height | Target | class | age | sex | |
|---|---|---|---|---|---|---|---|---|---|
| 53449 | c1edf42b-5958-47ff-a1e7-4f23d99583ba | NaN | NaN | NaN | NaN | 0 | Normal | 46 | F |
| 53450 | c1f6b555-2eb1-4231-98f6-50a963976431 | NaN | NaN | NaN | NaN | 0 | Normal | 45 | M |
| 53451 | c1f7889a-9ea9-4acb-b64c-b737c929599a | 570.0 | 393.0 | 261.0 | 345.0 | 1 | Lung Opacity | 72 | F |
| 53452 | c1f7889a-9ea9-4acb-b64c-b737c929599a | 570.0 | 393.0 | 261.0 | 345.0 | 1 | Lung Opacity | 72 | F |
| 53453 | c1f7889a-9ea9-4acb-b64c-b737c929599a | 570.0 | 393.0 | 261.0 | 345.0 | 1 | Lung Opacity | 72 | F |
| 53454 | c1f7889a-9ea9-4acb-b64c-b737c929599a | 570.0 | 393.0 | 261.0 | 345.0 | 1 | Lung Opacity | 72 | F |
| 53455 | c1f7889a-9ea9-4acb-b64c-b737c929599a | 233.0 | 424.0 | 201.0 | 356.0 | 1 | Lung Opacity | 72 | F |
| 53456 | c1f7889a-9ea9-4acb-b64c-b737c929599a | 233.0 | 424.0 | 201.0 | 356.0 | 1 | Lung Opacity | 72 | F |
| 53457 | c1f7889a-9ea9-4acb-b64c-b737c929599a | 233.0 | 424.0 | 201.0 | 356.0 | 1 | Lung Opacity | 72 | F |
| 53458 | c1f7889a-9ea9-4acb-b64c-b737c929599a | 233.0 | 424.0 | 201.0 | 356.0 | 1 | Lung Opacity | 72 | F |
print(merged_data_info.shape)
print(merged_data_info.isna().sum())
(53459, 9) patientId 0 x 20672 y 20672 width 20672 height 20672 Target 0 class 0 age 0 sex 0 dtype: int64
merged_data_info['age'] = merged_data_info['age'].astype('int64')
print('Minimum Age in the dataset:', merged_data_info['age'].min())
print('Maximum Age in the dataset', merged_data_info['age'].max())
Minimum Age in the dataset: 1 Maximum Age in the dataset 155
## Check the distribution of age using histogram
sns.distplot(merged_data_info['age'], hist=True, kde=True,
bins=100, color = 'darkblue',
hist_kws={'edgecolor':'black'},
kde_kws={'linewidth': 4})
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
<matplotlib.axes._subplots.AxesSubplot at 0x7fc786c4ffd0>
merged_data_info['age'].value_counts()
58 2035
56 1715
52 1439
40 1422
46 1271
...
148 1
151 1
153 1
150 1
155 1
Name: age, Length: 97, dtype: int64
## Let's plot the box plot for the age distribution for male and females in the patients
## Plot the Box Plot for L_angle for three different values of Class variable
plt.figure(figsize=(8,8))
sns.boxplot(x='sex',y='age',data=merged_data_info, palette='rainbow')
plt.title("Age by Gender variable")
Text(0.5, 1.0, 'Age by Gender variable')
## Let's evaluate the target distribution from the info worksheet
label_count=datainfo['class'].value_counts()
explode = (0.01,0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('Class Distribution')
plt.show()
# lets take a look at our Target Distribution from the labels csv provided
label_count=datalabels['Target'].value_counts()
explode = (0.1,0.0)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=['Normal','Pneumonia'], autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('Target Distribution')
plt.show()
## Let's check the distribution of the target for males and females
sns.countplot(x=merged_data_info['Target'], hue=merged_data_info['sex'])
<matplotlib.axes._subplots.AxesSubplot at 0x7fc7861d3e50>
print ("Path for the first training image: ",trainfilelist[0])
print ("No of images in training data: ",len(trainfilelist))
Path for the first training image: /content/CapstoneProject/rsna-pneumonia-detection-challenge/stage_2_train_images/0f510183-11b0-4356-b8ae-24ac9e3814c4.dcm No of images in training data: 26684
## Let's create two seperate dataframes for pneumonia and non pneumonia patients
DfPositivePneunomia = datalabels[datalabels['Target'] == 1]
DfNegativePneunomia = datalabels[datalabels['Target'] == 0]
print (datalabels.head())
print (DfPositivePneunomia.head())
print (DfNegativePneunomia.tail())
patientId x y width height Target
0 0004cfab-14fd-4e49-80ba-63a80b6bddd6 NaN NaN NaN NaN 0
1 00313ee0-9eaa-42f4-b0ab-c148ed3241cd NaN NaN NaN NaN 0
2 00322d4d-1c29-4943-afc9-b6754be640eb NaN NaN NaN NaN 0
3 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 NaN NaN NaN NaN 0
4 00436515-870c-4b36-a041-de91049b9ab4 264.0 152.0 213.0 379.0 1
patientId x y width height Target
4 00436515-870c-4b36-a041-de91049b9ab4 264.0 152.0 213.0 379.0 1
5 00436515-870c-4b36-a041-de91049b9ab4 562.0 152.0 256.0 453.0 1
8 00704310-78a8-4b38-8475-49f4573b2dbb 323.0 577.0 160.0 104.0 1
9 00704310-78a8-4b38-8475-49f4573b2dbb 695.0 575.0 162.0 137.0 1
14 00aecb01-a116-45a2-956c-08d2fa55433f 288.0 322.0 94.0 135.0 1
patientId x y width height Target
30216 c1cf3255-d734-4980-bfe0-967902ad7ed9 NaN NaN NaN NaN 0
30217 c1e228e4-b7b4-432b-a735-36c48fdb806f NaN NaN NaN NaN 0
30218 c1e3eb82-c55a-471f-a57f-fe1a823469da NaN NaN NaN NaN 0
30223 c1edf42b-5958-47ff-a1e7-4f23d99583ba NaN NaN NaN NaN 0
30224 c1f6b555-2eb1-4231-98f6-50a963976431 NaN NaN NaN NaN 0
## Let's print one single image
from PIL import Image
ds = dicom.dcmread(trainfilelist[0])
pil_image=Image.fromarray(ds.pixel_array)
imgarray = np.array(pil_image)
plt.imshow(imgarray)
<matplotlib.image.AxesImage at 0x7fc785ec3990>
from PIL import ImageDraw as D
## Let's print one image with boundary box
ds = dicom.dcmread(Project_Train_Images_Path + DfPositivePneunomia['patientId'][4] + '.dcm')
X0 = DfPositivePneunomia['x'][4]
Y0 = DfPositivePneunomia['y'][4]
X1 = DfPositivePneunomia['x'][4] + DfPositivePneunomia['width'][4]
Y1 = DfPositivePneunomia['y'][4] + DfPositivePneunomia['height'][4]
pil_image=Image.fromarray(ds.pixel_array)
draw=D.Draw(pil_image)
draw.rectangle([(X0,Y0),(X1,Y1)],outline="white", width = 4)
imgarray = np.array(pil_image)
plt.imshow(imgarray)
<matplotlib.image.AxesImage at 0x7fc7860cd150>
## Define a function to plot images
def PlotImage (ImageData):
## ImageData is the dictionary where key is the patientid and value is the list of arrays with x0, y0 and x1, y1 cordinates.
## If the length of the list is zero, then there is no pneumonia for the patient
## Expectation is that this functiona will print the images in the dictionary and highlight the infected areas.
fig = plt.figure(figsize=(24, 30))
rows = 4
columns = 4
i = 0
for PatientID in ImageData:
i = i+1
ImagePath = Project_Train_Images_Path + PatientID + '.dcm'
ds = dicom.dcmread(ImagePath)
data_row_img_data = dicom.read_file(ImagePath)
modality = data_row_img_data.Modality
age = data_row_img_data.PatientAge
sex = data_row_img_data.PatientSex
pil_image=Image.fromarray(ds.pixel_array)
draw=D.Draw(pil_image)
PneumoniaDetected = True
BoundingBox = ImageData[PatientID]
if (len(BoundingBox) == 0):
## If BoundingBox is not present, then this is Normal patient
PneumoniaDetected = False
else:
## If BoundingBox is present, then this is Pneunomia Infected patient
PneumoniaDetected = True
## Draw as many rectangles as many bounding Boxes present
for iCnt in range(len(BoundingBox)):
draw.rectangle([(BoundingBox[iCnt][0],BoundingBox[iCnt][1]),(BoundingBox[iCnt][2],BoundingBox[iCnt][3])],outline="white", width = 4)
imgarray = np.array(pil_image)
fig.add_subplot(rows, columns, i)
plt.imshow(imgarray)
plt.axis('off')
plt.title('ID: {}\nModality: {} Age: {} Sex: {} Target: {}'.format(
PatientID,
modality, age, sex, PneumoniaDetected
))
plt.show()
## Let's prepare ImageData dictionary to load the data and show it
ImageData = {}
## Let's populate the dictionary with three images where Pneumonia is not detected
iCount = 0
iTargetCount = 3
for ind in DfNegativePneunomia.index:
BoundingBox = []
if (iCount == iTargetCount):
break
ImageData[DfNegativePneunomia['patientId'][ind]]= BoundingBox
iCount +=1
## Let's set three images with a single BoundingBox
## Let's set three images with multiple BoundingBoxes
## Let's refer to the dataframe - dfmultirowpatients
iCount = 0
iTargetCount = 10
for ind in dfmultirowpatients.index:
BoundingBoxList = []
if (iCount == iTargetCount):
break
df = dfmultirowpatients.loc[dfmultirowpatients['patientId'] == dfmultirowpatients['patientId'][ind]]
for indInternal in df.index:
BoundingBoxArr = np.zeros(shape=(4,1))
BoundingBoxArr[0] = dfmultirowpatients['x'][indInternal]
BoundingBoxArr[1] = dfmultirowpatients['y'][indInternal]
BoundingBoxArr[2] = dfmultirowpatients['x'][indInternal] + dfmultirowpatients['width'][indInternal]
BoundingBoxArr[3] = dfmultirowpatients['y'][indInternal] + dfmultirowpatients['height'][indInternal]
BoundingBoxList.append(BoundingBoxArr)
#ImageData[DfNegativePneunomia['patientId'][ind]]= BoundingBox
ImageData[dfmultirowpatients['patientId'][ind]]= BoundingBoxList
iCount +=1
dfSingleRowpatients = datalabels.groupby('patientId').filter(lambda x: len(x) == 1)
dfSingleRowpatients = dfSingleRowpatients.loc[dfSingleRowpatients['Target'] == 1]
## Let's load a couple of patients with a songle BoundingBox
iCount = 0
iTargetCount = 4
for ind in dfSingleRowpatients.index:
BoundingBoxList = []
if (iCount == iTargetCount):
break
df = dfSingleRowpatients.loc[dfSingleRowpatients['patientId'] == dfSingleRowpatients['patientId'][ind]]
for indInternal in df.index:
BoundingBoxArr = np.zeros(shape=(4,1))
BoundingBoxArr[0] = dfSingleRowpatients['x'][indInternal]
BoundingBoxArr[1] = dfSingleRowpatients['y'][indInternal]
BoundingBoxArr[2] = dfSingleRowpatients['x'][indInternal] + dfSingleRowpatients['width'][indInternal]
BoundingBoxArr[3] = dfSingleRowpatients['y'][indInternal] + dfSingleRowpatients['height'][indInternal]
BoundingBoxList.append(BoundingBoxArr)
#ImageData[DfNegativePneunomia['patientId'][ind]]= BoundingBox
ImageData[dfSingleRowpatients['patientId'][ind]]= BoundingBoxList
iCount +=1
## Invoke the function to plot the images
## Images with different variations are plotted
PlotImage(ImageData)
Let us write some code to load the binary image data in a dictionary
The key of the dictionary would be the patientID and value would be the array of size 32x32 which represents the image of the Xray.
This data would be helpful in further model building
ImagesData = LoadImages (trainfilelist)
100%|██████████| 26684/26684 [07:37<00:00, 58.32it/s]
## Parallel processing is implemented to improve the performance of overall IO activity
import time
from multiprocessing import Pool
if __name__ == "__main__":
starttime = time.time()
pool = Pool()
result = pool.map_async(LoadImages, chunked_list)
endtime = time.time()
print("main script")
Images = result.get()
print("end main script")
print("Time taken {endtime-starttime} seconds")
## At the end of this execution, Images would be the list of dictionary objects.
## Each object would have the key as patientID and value as the image data
0%| | 3/11684 [00:00<06:34, 29.64it/s]
main script
100%|██████████| 11684/11684 [05:27<00:00, 35.66it/s] 100%|██████████| 15000/15000 [06:29<00:00, 38.47it/s]
end main script
Time taken {endtime-starttime} seconds
!pip install pydicom
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: pydicom in /usr/local/lib/python3.7/dist-packages (2.3.0)
import os
import numpy as np
import pandas as pd
import cv2
from glob import glob
import tensorflow as tf
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import seaborn as sns
import pydicom as dcm
import math
from tensorflow.keras.layers import Layer, Convolution2D, Flatten, Dense
from tensorflow.keras.layers import Concatenate, UpSampling2D, Conv2D, Reshape, GlobalAveragePooling2D
from tensorflow.keras.models import Model
import cv2
from tensorflow.keras.applications.mobilenet import MobileNet
from tensorflow.keras.applications.mobilenet import preprocess_input
import tensorflow.keras.utils as pltUtil
from tensorflow.keras.utils import Sequence
import math
from tensorflow.keras.applications.resnet import ResNet50
from tensorflow.keras.applications.resnet import preprocess_input as resnetProcess_input
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
!cd /content
!pwd
!ls -lrt
!unzip "/content/drive/My Drive/AIML - Projects/Capstone Project - CV1/rsna-pneumonia-detection-challenge.zip" -d "/content/CapstoneProject" > /dev/null
/content total 12 drwxr-xr-x 1 root root 4096 Jun 15 13:42 sample_data drwx------ 5 root root 4096 Jun 25 10:46 drive drwxr-xr-x 4 root root 4096 Jun 25 10:46 CapstoneProject replace /content/CapstoneProject/__MACOSX/._rsna-pneumonia-detection-challenge? [y]es, [n]o, [A]ll, [N]one, [r]ename: N
Base_path = '/content/CapstoneProject/rsna-pneumonia-detection-challenge/'
## reading the labels data set and showing first few records
labels = pd.read_csv(Base_path + "stage_2_train_labels.csv")
labels.head()
| patientId | x | y | width | height | Target | |
|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 |
labels.shape
## There are 30227 records and 6 rows
(30227, 6)
labels.info()
## There are 30227 rows
## there are 30277 target as well
## x ,y, WIdth and height count is 9555 , the others are null
<class 'pandas.core.frame.DataFrame'> RangeIndex: 30227 entries, 0 to 30226 Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 patientId 30227 non-null object 1 x 9555 non-null float64 2 y 9555 non-null float64 3 width 9555 non-null float64 4 height 9555 non-null float64 5 Target 30227 non-null int64 dtypes: float64(4), int64(1), object(1) memory usage: 1.4+ MB
## we can see that all the null column values are with Target 0 indicating that those patients do not have penumonia
labels[labels.isnull().any(axis=1)].Target.value_counts()
0 20672 Name: Target, dtype: int64
## we can see that all the non null column values are with Target 1 indicating that those patients have pneumonia
labels[~labels.isnull().any(axis=1)].Target.value_counts()
1 9555 Name: Target, dtype: int64
## Distubution of Targets , there are 20672 records with no pneumonia and 9555 with pneumonia
labels.Target.value_counts()
0 20672 1 9555 Name: Target, dtype: int64
## Disturbution of Target, there are 31% of patients with pneumonia and the remaining are no pneumonia
## There is a class imbalance issue
label_count=labels['Target'].value_counts()
explode = (0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('Target Distribution')
plt.show()
print("Are there Unique Patients In Data Set ?? " ,labels['patientId'].is_unique)
## There is no point in finding unique for x,y,width and height as there can be duplcaie values in them
## There are duplicate patients in the data set
Are there Unique Patients In Data Set ?? False
#labels.loc[labels.index.repeat(labels.patientId)]
duplicateRowsDF = labels[labels.duplicated(['patientId'])]
duplicateRowsDF.shape
## There are 3543 duplicates
(3543, 6)
duplicateRowsDF.head(2)
| patientId | x | y | width | height | Target | |
|---|---|---|---|---|---|---|
| 5 | 00436515-870c-4b36-a041-de91049b9ab4 | 562.0 | 152.0 | 256.0 | 453.0 | 1 |
| 9 | 00704310-78a8-4b38-8475-49f4573b2dbb | 695.0 | 575.0 | 162.0 | 137.0 | 1 |
## Examining one of the patient id which is duplicate , we can see that the x,y, widht and height is not the same
## This indicates that the same patient has two bounding boxes in the same dicom image
labels[labels.patientId=='00436515-870c-4b36-a041-de91049b9ab4']
| patientId | x | y | width | height | Target | |
|---|---|---|---|---|---|---|
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 |
| 5 | 00436515-870c-4b36-a041-de91049b9ab4 | 562.0 | 152.0 | 256.0 | 453.0 | 1 |
labels[labels.patientId=='00704310-78a8-4b38-8475-49f4573b2dbb']
| patientId | x | y | width | height | Target | |
|---|---|---|---|---|---|---|
| 8 | 00704310-78a8-4b38-8475-49f4573b2dbb | 323.0 | 577.0 | 160.0 | 104.0 | 1 |
| 9 | 00704310-78a8-4b38-8475-49f4573b2dbb | 695.0 | 575.0 | 162.0 | 137.0 | 1 |
## Reading the classes label ,
class_labels = pd.read_csv(Base_path + "stage_2_detailed_class_info.csv")
class_labels.head()
| patientId | class | |
|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | No Lung Opacity / Not Normal |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | No Lung Opacity / Not Normal |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | No Lung Opacity / Not Normal |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | Normal |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | Lung Opacity |
class_labels.shape
## There are 30277 rows and two columns , 30277 rows same as the labels data set
(30227, 2)
class_labels.info()
## There are no null columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 30227 entries, 0 to 30226 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 patientId 30227 non-null object 1 class 30227 non-null object dtypes: object(2) memory usage: 472.4+ KB
class_labels['class'].value_counts()
## there are 8851 normal cases , person with lung opactiry are 9555 and No Lung Opacity / Not Normal are 11821
No Lung Opacity / Not Normal 11821 Lung Opacity 9555 Normal 8851 Name: class, dtype: int64
## Disturbution of Classes, there are 39% of patients with No Lung opacity , 29.3% Normal
## and the remaining are with Lung Opacity
label_count=class_labels['class'].value_counts()
explode = (0.01,0.01,0.01)
fig1, ax1 = plt.subplots(figsize=(5,5))
ax1.pie(label_count.values, explode=explode, labels=label_count.index, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal')
plt.title('Class Distribution')
plt.show()
#labels.loc[labels.index.repeat(labels.patientId)]
duplicateClassRowsDF = class_labels[class_labels.duplicated(['patientId'])]
duplicateClassRowsDF.shape
## There are 3543 duplicates similar to the labels dataset
(3543, 2)
duplicateClassRowsDF.head(2)
| patientId | class | |
|---|---|---|
| 5 | 00436515-870c-4b36-a041-de91049b9ab4 | Lung Opacity |
| 9 | 00704310-78a8-4b38-8475-49f4573b2dbb | Lung Opacity |
## The same patient id has the same class even though they are duplicate
class_labels[class_labels.patientId=='00704310-78a8-4b38-8475-49f4573b2dbb']
| patientId | class | |
|---|---|---|
| 8 | 00704310-78a8-4b38-8475-49f4573b2dbb | Lung Opacity |
| 9 | 00704310-78a8-4b38-8475-49f4573b2dbb | Lung Opacity |
Merging the class and labels data set into training dataset
# Conctinating the two dataset - 'labels' and 'class_labels':
training_data = pd.concat([labels, class_labels['class']], axis = 1)
training_data.head()
| patientId | x | y | width | height | Target | class | |
|---|---|---|---|---|---|---|---|
| 0 | 0004cfab-14fd-4e49-80ba-63a80b6bddd6 | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal |
| 1 | 00313ee0-9eaa-42f4-b0ab-c148ed3241cd | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal |
| 2 | 00322d4d-1c29-4943-afc9-b6754be640eb | NaN | NaN | NaN | NaN | 0 | No Lung Opacity / Not Normal |
| 3 | 003d8fa0-6bf1-40ed-b54c-ac657f8495c5 | NaN | NaN | NaN | NaN | 0 | Normal |
| 4 | 00436515-870c-4b36-a041-de91049b9ab4 | 264.0 | 152.0 | 213.0 | 379.0 | 1 | Lung Opacity |
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('Target')['class'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'Target', y = 'Values', hue = 'class', data = data_target_class, palette = 'Set3')
plt.title('Class and Target Distrubution')
## it shows that class distrubution grouped by Target
## Target 0 has only Normal or No Lung Opacity class
## Target 1 has only Lung Opacity class
Text(0.5, 1.0, 'Class and Target Distrubution')
## ANalysing the dicom image
import matplotlib.patches as patches
def inspectImages(data):
img_data = list(data.T.to_dict().values())
f, ax = plt.subplots(3,3, figsize=(16,18))
for i,data_row in enumerate(img_data):
patientImage = data_row['patientId']
dcm_file = Base_path + 'stage_2_train_images/'+'{}.dcm'.format(patientImage)
data_row_img_data = dcm.read_file(dcm_file)
modality = data_row_img_data.Modality
age = data_row_img_data.PatientAge
sex = data_row_img_data.PatientSex
data_row_img = dcm.dcmread(dcm_file)
ax[i//3, i%3].imshow(data_row_img.pixel_array, cmap=plt.cm.bone)
ax[i//3, i%3].axis('off')
ax[i//3, i%3].set_title('ID: {}\nModality: {} Age: {} Sex: {} Target: {}\nClass: {}\Bounds: {}:{}:{}:{}'.format(
data_row['patientId'],
modality, age, sex, data_row['Target'], data_row['class'],
data_row['x'],data_row['y'],data_row['width'],data_row['height']))
label = data_row["class"]
if not math.isnan(data_row['x']):
x, y, width, height = data_row['x'],data_row['y'],data_row['width'],data_row['height']
rect = patches.Rectangle((x, y),width, height,
linewidth = 2,
edgecolor = 'r',
facecolor = 'none')
# Draw the bounding box on top of the image
ax[i//3, i%3].add_patch(rect)
plt.show()
Displaying Chest Xray Images of Patients who have Pneuomina
## checking few images which has pneuonia
inspectImages(training_data[training_data['Target']==1].sample(9))
## checking few images which does not have pneuonia
inspectImages(training_data[training_data['Target']==0].sample(9))
Reading the Dicom images meta data and appending it to the training set
## DCIM image contain the meta data alon with it,
## Function to read the dcim data and appending to the resultset
def readDCIMData(rowData):
dcm_file = Base_path + 'stage_2_train_images/'+'{}.dcm'.format(rowData.patientId)
dcm_data = dcm.read_file(dcm_file)
img = dcm_data.pixel_array
return dcm_data.PatientSex,dcm_data.PatientAge
## Reading the image data and append it to the training_data dataset
training_data['sex'], training_data['age'] = zip(*training_data.apply(readDCIMData, axis=1))
training_data.info()
## There are 30227 records
## sex and age also have the smae count indicating that there are no images missing every patient has an dicom image
## Age should be a numeric , currently it is an objetc
<class 'pandas.core.frame.DataFrame'> RangeIndex: 30227 entries, 0 to 30226 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 patientId 30227 non-null object 1 x 9555 non-null float64 2 y 9555 non-null float64 3 width 9555 non-null float64 4 height 9555 non-null float64 5 Target 30227 non-null int64 6 class 30227 non-null object 7 sex 30227 non-null object 8 age 30227 non-null object dtypes: float64(4), int64(1), object(4) memory usage: 2.1+ MB
# Converting age to Numeric as the current data type is a String
training_data['age'] = training_data.age.astype(int)
training_data.describe(include="all").T
## The mean age is 46 years , where as minimum age is 1 year and the max age is 155 which seems to be an outlier
## 50% of the patiens are of aroudn 49 age , the std deviation is 16 which sugges that age is not normally distubuted
| count | unique | top | freq | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| patientId | 30227 | 26684 | 3239951b-6211-4290-b237-3d9ad17176db | 4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| x | 9555.0 | NaN | NaN | NaN | 394.047724 | 204.574172 | 2.0 | 207.0 | 324.0 | 594.0 | 835.0 |
| y | 9555.0 | NaN | NaN | NaN | 366.83956 | 148.940488 | 2.0 | 249.0 | 365.0 | 478.5 | 881.0 |
| width | 9555.0 | NaN | NaN | NaN | 218.471376 | 59.289475 | 40.0 | 177.0 | 217.0 | 259.0 | 528.0 |
| height | 9555.0 | NaN | NaN | NaN | 329.269702 | 157.750755 | 45.0 | 203.0 | 298.0 | 438.0 | 942.0 |
| Target | 30227.0 | NaN | NaN | NaN | 0.316108 | 0.464963 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| class | 30227 | 3 | No Lung Opacity / Not Normal | 11821 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| sex | 30227 | 2 | M | 17216 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| age | 30227.0 | NaN | NaN | NaN | 46.797764 | 16.89294 | 1.0 | 34.0 | 49.0 | 59.0 | 155.0 |
training_data.sex.value_counts()
## there are only two genders
M 17216 F 13011 Name: sex, dtype: int64
## Distbution of Sex Among the tragets
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('Target')['sex'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'Target', y = 'Values', hue = 'sex', data = data_target_class, palette = 'Set3')
plt.title('Sex and Target for Chest Exams')
## the number of males in both category are higher than women
Text(0.5, 1.0, 'Sex and Target for Chest Exams')
## Distbution of Sex Among the classes
fig, ax = plt.subplots(nrows = 1, figsize = (12, 6))
temp = training_data.groupby('class')['sex'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'class', y = 'Values', hue = 'sex', data = data_target_class, palette = 'Set3')
plt.title('Sex and class for Chest Exams')
## the number of males in all classes are higher than women
Text(0.5, 1.0, 'Sex and class for Chest Exams')
sns.distplot(training_data.age)
# plots the distrubution of age
## Looks like normal distubution of age
<matplotlib.axes._subplots.AxesSubplot at 0x7f8706e4d310>
plt.figure(figsize=(10,5)) # setting the figure size
ax = sns.barplot(x='class', y='age', data=training_data, palette='muted') # barplot'
## This is the distubution of Age with class, maximum age of person with pneuomina is arund 45
plt.figure(figsize=(10,5)) # setting the figure size
ax = sns.barplot(x='Target', y='age', data=training_data, palette='muted') # barplot'
## This is the distubution of Age with class, maximum age of person with pneuomina is arund 45
plt.figure(figsize=(15,7))
sns.boxplot(x='class', y='age', data= training_data)
plt.show()
## The class which has no pneuomia has few outliers , theie age is somewhere aroun 150 years
print('Distribution of `Age`: Overall and Target = 1')
fig = plt.figure(figsize = (10, 6))
ax = fig.add_subplot(121)
g = (sns.distplot(training_data['age']).set_title('Distribution of PatientAge'))
ax = fig.add_subplot(122)
g = (sns.distplot(training_data.loc[training_data['Target'] == 1, 'age']).set_title('Distribution of PatientAge who have pneumonia'))
## Overall Distrubution of Age looks normal with very little skwe
## Distubution of Patients afe who have penuomonia are a left skewed
Distribution of `Age`: Overall and Target = 1
corr = training_data.corr()
plt.figure(figsize=(12,5))
sns.heatmap(corr,annot=True)
## There is high corelation between widht and height
<matplotlib.axes._subplots.AxesSubplot at 0x7f842e895190>
## Just taking a 200 samples from the dataset
sample_trainigdata = training_data.groupby('class', group_keys=False).apply(lambda x: x.sample(800))
## Checking the training data set with class distbution
sample_trainigdata["class"].value_counts()
Lung Opacity 800 No Lung Opacity / Not Normal 800 Normal 800 Name: class, dtype: int64
sample_trainigdata.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 2400 entries, 19348 to 3278 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 patientId 2400 non-null object 1 x 800 non-null float64 2 y 800 non-null float64 3 width 800 non-null float64 4 height 800 non-null float64 5 Target 2400 non-null int64 6 class 2400 non-null object 7 sex 2400 non-null object 8 age 2400 non-null int64 dtypes: float64(4), int64(2), object(3) memory usage: 187.5+ KB
## Pre Processing the image
from tensorflow.keras.applications.mobilenet import preprocess_input
images = []
ADJUSTED_IMAGE_SIZE = 128
imageList = []
classLabels = []
labels = []
originalImage = []
# Function to read the image from the path and reshape the image to size
def readAndReshapeImage(image):
img = np.array(image).astype(np.uint8)
## Resize the image
res = cv2.resize(img,(ADJUSTED_IMAGE_SIZE,ADJUSTED_IMAGE_SIZE), interpolation = cv2.INTER_LINEAR)
return res
## Read the imahge and resize the image
def populateImage(rowData):
for index, row in rowData.iterrows():
patientId = row.patientId
classlabel = row["class"]
dcm_file = Base_path + 'stage_2_train_images/'+'{}.dcm'.format(patientId)
dcm_data = dcm.read_file(dcm_file)
img = dcm_data.pixel_array
## Converting the image to 3 channels as the dicom image pixel does not have colour classes wiht it
if len(img.shape) != 3 or img.shape[2] != 3:
img = np.stack((img,) * 3, -1)
imageList.append(readAndReshapeImage(img))
# originalImage.append(img)
classLabels.append(classlabel)
tmpImages = np.array(imageList)
tmpLabels = np.array(classLabels)
# originalImages = np.array(originalImage)
return tmpImages,tmpLabels
## Reading the images into numpy array
images,labels = populateImage(sample_trainigdata)
images.shape , labels.shape
## The image is of 128*128 with 3 channels
((2400, 128, 128, 3), (2400,))
## Checking one of the converted image
plt.imshow(images[100])
<matplotlib.image.AxesImage at 0x7f8534000fd0>
## check the unique labels
np.unique(labels),len(np.unique(labels))
(array(['Lung Opacity', 'No Lung Opacity / Not Normal', 'Normal'],
dtype='<U28'), 3)
from sklearn.preprocessing import LabelEncoder
import numpy as np
import tensorflow
from tensorflow.keras.models import Sequential
# define model
from tensorflow.keras import losses,optimizers
from tensorflow.keras.layers import Dense, Activation, Flatten,Dropout,MaxPooling2D,BatchNormalization
import pandas as pd
import tensorflow as tf
import numpy as np
import pandas as pd
import seaborn as sns
import scipy.stats as stats
import matplotlib.pyplot as plt
from tensorflow import keras
#from keras.models import Sequential
#from keras.layers import Dense
#from sklearn.model_selection import StratifiedKFold
%matplotlib inline
#Test Train Split
from sklearn.model_selection import train_test_split
#Feature Scaling library
from sklearn.preprocessing import StandardScaler
#import pickle
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten, Dense ,LeakyReLU
from tensorflow.keras import regularizers, optimizers
from sklearn.metrics import r2_score
from tensorflow.keras.models import load_model
import warnings
warnings.filterwarnings('ignore')
from keras.models import Sequential # initial NN
from keras.layers import Dense, Dropout # construct each layer
from keras.layers import Conv2D # swipe across the image by 1
from keras.layers import MaxPooling2D # swipe across by pool size
from keras.layers import Flatten, GlobalAveragePooling2D,GlobalMaxPooling2D
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam
## encoding the labels
from sklearn.preprocessing import LabelBinarizer
enc = LabelBinarizer()
y2 = enc.fit_transform(labels)
## splitting into train ,test and validation data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(images, y2, test_size=0.3, random_state=50)
X_test, X_val, y_test, y_val = train_test_split(X_test,y_test, test_size = 0.5, random_state=50)
## FUnction to create a dataframe for results
def createResultDf(name,accuracy,testscore):
result = pd.DataFrame({'Method':[name], 'accuracy': [accuracy] ,'Test Score':[testscore]})
return result
## CNN Model without transfer learning ,
## we start with 32 filters with 5,5 kernal and no padding ,
## then 64 and 128 wiht drop layers in between
## And softmax activaation as the last layer
def cnn_model(height, width, num_channels, num_classes, loss='categorical_crossentropy', metrics=['accuracy']):
batch_size = None
model = Sequential()
model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same',
activation ='relu', batch_input_shape = (batch_size,height, width, num_channels)))
model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same',
activation ='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same',
activation ='relu'))
model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'same',
activation ='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.3))
model.add(Conv2D(filters = 128, kernel_size = (3,3),padding = 'Same',
activation ='relu'))
model.add(Conv2D(filters = 128, kernel_size = (3,3),padding = 'Same',
activation ='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.4))
model.add(GlobalMaxPooling2D())
model.add(Dense(256, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation = "softmax"))
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(optimizer = optimizer, loss = loss, metrics = metrics)
model.summary()
return model
# Model Summary
cnn = cnn_model(ADJUSTED_IMAGE_SIZE,ADJUSTED_IMAGE_SIZE,3,3)
Model: "sequential_7"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_12 (Conv2D) (None, 128, 128, 32) 2432
conv2d_13 (Conv2D) (None, 128, 128, 32) 25632
max_pooling2d_6 (MaxPooling (None, 64, 64, 32) 0
2D)
dropout_8 (Dropout) (None, 64, 64, 32) 0
conv2d_14 (Conv2D) (None, 64, 64, 64) 18496
conv2d_15 (Conv2D) (None, 64, 64, 64) 36928
max_pooling2d_7 (MaxPooling (None, 32, 32, 64) 0
2D)
dropout_9 (Dropout) (None, 32, 32, 64) 0
conv2d_16 (Conv2D) (None, 32, 32, 128) 73856
conv2d_17 (Conv2D) (None, 32, 32, 128) 147584
max_pooling2d_8 (MaxPooling (None, 16, 16, 128) 0
2D)
dropout_10 (Dropout) (None, 16, 16, 128) 0
global_max_pooling2d_2 (Glo (None, 128) 0
balMaxPooling2D)
dense_19 (Dense) (None, 256) 33024
dropout_11 (Dropout) (None, 256) 0
dense_20 (Dense) (None, 3) 771
=================================================================
Total params: 338,723
Trainable params: 338,723
Non-trainable params: 0
_________________________________________________________________
## Training for 30 epocs with batch size of 30
history = cnn.fit(X_train,
y_train,
epochs = 30,
validation_data = (X_val,y_val),
batch_size = 30)
Epoch 1/30 56/56 [==============================] - 3s 34ms/step - loss: 4.7667 - accuracy: 0.3351 - val_loss: 1.0845 - val_accuracy: 0.3667 Epoch 2/30 56/56 [==============================] - 2s 31ms/step - loss: 1.1192 - accuracy: 0.3702 - val_loss: 1.1227 - val_accuracy: 0.3222 Epoch 3/30 56/56 [==============================] - 2s 31ms/step - loss: 1.1122 - accuracy: 0.3375 - val_loss: 1.1041 - val_accuracy: 0.3889 Epoch 4/30 56/56 [==============================] - 2s 31ms/step - loss: 1.1020 - accuracy: 0.3792 - val_loss: 1.0756 - val_accuracy: 0.4194 Epoch 5/30 56/56 [==============================] - 2s 31ms/step - loss: 1.0940 - accuracy: 0.3929 - val_loss: 1.0701 - val_accuracy: 0.5083 Epoch 6/30 56/56 [==============================] - 2s 31ms/step - loss: 1.0749 - accuracy: 0.4226 - val_loss: 1.1240 - val_accuracy: 0.3222 Epoch 7/30 56/56 [==============================] - 2s 31ms/step - loss: 1.0501 - accuracy: 0.4446 - val_loss: 1.0540 - val_accuracy: 0.4083 Epoch 8/30 56/56 [==============================] - 2s 31ms/step - loss: 1.0667 - accuracy: 0.4512 - val_loss: 1.0105 - val_accuracy: 0.5250 Epoch 9/30 56/56 [==============================] - 2s 31ms/step - loss: 1.0546 - accuracy: 0.4482 - val_loss: 1.0153 - val_accuracy: 0.5194 Epoch 10/30 56/56 [==============================] - 2s 31ms/step - loss: 1.0242 - accuracy: 0.4786 - val_loss: 1.0138 - val_accuracy: 0.4861 Epoch 11/30 56/56 [==============================] - 2s 31ms/step - loss: 1.0107 - accuracy: 0.4863 - val_loss: 0.9991 - val_accuracy: 0.4972 Epoch 12/30 56/56 [==============================] - 2s 31ms/step - loss: 1.0168 - accuracy: 0.4786 - val_loss: 1.0009 - val_accuracy: 0.5222 Epoch 13/30 56/56 [==============================] - 2s 31ms/step - loss: 1.0120 - accuracy: 0.4798 - val_loss: 0.9936 - val_accuracy: 0.5417 Epoch 14/30 56/56 [==============================] - 2s 31ms/step - loss: 1.0027 - accuracy: 0.4821 - val_loss: 1.0000 - val_accuracy: 0.4583 Epoch 15/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9880 - accuracy: 0.5048 - val_loss: 0.9800 - val_accuracy: 0.5222 Epoch 16/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9799 - accuracy: 0.5065 - val_loss: 0.9813 - val_accuracy: 0.5278 Epoch 17/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9830 - accuracy: 0.5036 - val_loss: 1.0062 - val_accuracy: 0.5056 Epoch 18/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9615 - accuracy: 0.5280 - val_loss: 0.9465 - val_accuracy: 0.5000 Epoch 19/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9529 - accuracy: 0.5208 - val_loss: 1.0092 - val_accuracy: 0.4639 Epoch 20/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9739 - accuracy: 0.5143 - val_loss: 0.9798 - val_accuracy: 0.5083 Epoch 21/30 56/56 [==============================] - 2s 32ms/step - loss: 0.9662 - accuracy: 0.5220 - val_loss: 0.9897 - val_accuracy: 0.5167 Epoch 22/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9459 - accuracy: 0.5268 - val_loss: 0.9837 - val_accuracy: 0.5333 Epoch 23/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9344 - accuracy: 0.5381 - val_loss: 1.0175 - val_accuracy: 0.5333 Epoch 24/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9269 - accuracy: 0.5518 - val_loss: 0.9544 - val_accuracy: 0.5139 Epoch 25/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9398 - accuracy: 0.5315 - val_loss: 0.9864 - val_accuracy: 0.5556 Epoch 26/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9285 - accuracy: 0.5363 - val_loss: 1.0065 - val_accuracy: 0.5167 Epoch 27/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9116 - accuracy: 0.5577 - val_loss: 1.0918 - val_accuracy: 0.4861 Epoch 28/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9141 - accuracy: 0.5554 - val_loss: 0.9708 - val_accuracy: 0.5194 Epoch 29/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9124 - accuracy: 0.5500 - val_loss: 0.9450 - val_accuracy: 0.5306 Epoch 30/30 56/56 [==============================] - 2s 31ms/step - loss: 0.9092 - accuracy: 0.5524 - val_loss: 1.0709 - val_accuracy: 0.4500
## evakualting the acuracy , we have only got accuracy of 43-46% where as the training accuracy is around 60%
fcl_loss, fcl_accuracy = cnn.evaluate(X_test, y_test, verbose=1)
print('Test loss:', fcl_loss)
print('Test accuracy:', fcl_accuracy)
12/12 [==============================] - 0s 10ms/step - loss: 1.1120 - accuracy: 0.4722 Test loss: 1.111989974975586 Test accuracy: 0.4722222089767456
## PLottting the accuracy vs loss graph
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(30)
plt.figure(figsize=(15, 15))
plt.subplot(2, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(2, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
#The training and Validation loss is almost same, but for the training and validation accuracy chart,
#the validation accuracy falls down in the later epochs, this could be because we have only taken 200 images for processing.
resultDF = createResultDf("CNN",acc[-1],fcl_accuracy)
from sklearn.metrics import confusion_matrix
import itertools
plt.subplots(figsize=(22,7)) #set the size of the plot
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
# Predict the values from the validation dataset
Y_pred = cnn.predict(X_test)
# Convert predictions classes to one hot vectors
Y_pred_classes = np.argmax(Y_pred,axis = 1)
# Convert validation observations to one hot vectors
Y_true = np.argmax(y_test,axis = 1)
# compute the confusion matrix
confusion_mtx = confusion_matrix(Y_true, Y_pred_classes)
# plot the confusion matrix
plot_confusion_matrix(confusion_mtx, classes = range(3))
#Class 0 ,1 and 2
#Class 0 is Lung Opacity
#Class 1 is No Lung Opacity/Normal, the model has predicted mostly wrong in this case to the Target 0. Type 2 error
#Class 2 is Normal
from sklearn.metrics import recall_score, confusion_matrix, precision_score, f1_score, accuracy_score, roc_auc_score,classification_report
from sklearn.metrics import classification_report
Y_truepred = np.argmax(y_test,axis = 1)
Y_testPred = cnn.predict(X_test)
# Convert predictions classes to one hot vectors
Y_pred_classes = np.argmax(Y_pred,axis = 1)
reportData = classification_report(Y_truepred, Y_pred_classes,output_dict=True)
for data in reportData:
if(data == '-1' or data == '1'):
if(type(reportData[data]) is dict):
for subData in reportData[data]:
resultDF[data+"_"+subData] = reportData[data][subData]
resultDF
| Method | accuracy | Test Score | 1_precision | 1_recall | 1_f1-score | 1_support | |
|---|---|---|---|---|---|---|---|
| 0 | CNN | 0.552381 | 0.472222 | 0.348214 | 0.367925 | 0.357798 | 106 |
## Creating a Copy
X_train1 = X_train.copy()
X_val1 = X_val.copy()
X_test1 = X_test.copy()
CNN with Tranfer learning using VGG16
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import preprocess_input
##VGGNet is a well-documented and globally used architecture for convolutional neural network
## Include_top=False to remove the classification layer that was trained on the ImageNet dataset and set the model as not trainable
base_model = VGG16(weights="imagenet", include_top=False, input_shape=X_train[0].shape)
base_model.trainable = False ## Not trainable weights
## Preprocessing input
train_ds = preprocess_input(X_train1)
train_val_df = preprocess_input(X_val1)
## Adding two hidden later and one softmax layer as an output layer
from tensorflow.keras import layers, models
flatten_layer = layers.Flatten()
dense_layer_1 = layers.Dense(50, activation='relu')
dense_layer_2 = layers.Dense(20, activation='relu')
prediction_layer = layers.Dense(3, activation='softmax')
cnn_VGG16_model = models.Sequential([
base_model,
flatten_layer,
dense_layer_1,
dense_layer_2,
prediction_layer
])
from tensorflow.keras.callbacks import EarlyStopping
cnn_VGG16_model.compile(
optimizer='Adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'],
)
## Early stopping whe validation accuracy does not change for 7 iteration
es = EarlyStopping(monitor='val_accuracy', mode='auto', patience=7, restore_best_weights=True)
#Trainign the model
#history = cnn_VGG16_model.fit(train_ds, y_train, epochs=30, validation_data=(train_val_df,y_val) ,callbacks=es)
history = cnn_VGG16_model.fit(train_ds, y_train, epochs=30, validation_data=(train_val_df,y_val))
Epoch 1/30 53/53 [==============================] - 3s 39ms/step - loss: 1.2714 - accuracy: 0.5506 - val_loss: 0.6409 - val_accuracy: 0.6361 Epoch 2/30 53/53 [==============================] - 2s 34ms/step - loss: 0.4590 - accuracy: 0.7345 - val_loss: 0.6437 - val_accuracy: 0.6639 Epoch 3/30 53/53 [==============================] - 2s 34ms/step - loss: 0.3352 - accuracy: 0.8149 - val_loss: 0.6123 - val_accuracy: 0.6250 Epoch 4/30 53/53 [==============================] - 2s 34ms/step - loss: 0.2059 - accuracy: 0.9101 - val_loss: 0.6618 - val_accuracy: 0.6944 Epoch 5/30 53/53 [==============================] - 2s 34ms/step - loss: 0.1469 - accuracy: 0.9500 - val_loss: 0.6529 - val_accuracy: 0.6694 Epoch 6/30 53/53 [==============================] - 2s 34ms/step - loss: 0.1165 - accuracy: 0.9619 - val_loss: 0.7419 - val_accuracy: 0.6389 Epoch 7/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0797 - accuracy: 0.9786 - val_loss: 0.7457 - val_accuracy: 0.6556 Epoch 8/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0423 - accuracy: 0.9964 - val_loss: 0.7636 - val_accuracy: 0.6778 Epoch 9/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0280 - accuracy: 0.9988 - val_loss: 0.7927 - val_accuracy: 0.6583 Epoch 10/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0218 - accuracy: 0.9988 - val_loss: 0.8294 - val_accuracy: 0.6806 Epoch 11/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0161 - accuracy: 0.9994 - val_loss: 0.8594 - val_accuracy: 0.6722 Epoch 12/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0130 - accuracy: 0.9994 - val_loss: 0.8837 - val_accuracy: 0.6750 Epoch 13/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0092 - accuracy: 1.0000 - val_loss: 0.9014 - val_accuracy: 0.6667 Epoch 14/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0075 - accuracy: 1.0000 - val_loss: 0.9167 - val_accuracy: 0.6722 Epoch 15/30 53/53 [==============================] - 2s 35ms/step - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.9520 - val_accuracy: 0.6778 Epoch 16/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.9636 - val_accuracy: 0.6722 Epoch 17/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.9717 - val_accuracy: 0.6778 Epoch 18/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.9949 - val_accuracy: 0.6833 Epoch 19/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 1.0136 - val_accuracy: 0.6806 Epoch 20/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 1.0208 - val_accuracy: 0.6861 Epoch 21/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 1.0348 - val_accuracy: 0.6861 Epoch 22/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 1.0500 - val_accuracy: 0.6833 Epoch 23/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 1.0662 - val_accuracy: 0.6778 Epoch 24/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.0696 - val_accuracy: 0.6861 Epoch 25/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.0799 - val_accuracy: 0.6833 Epoch 26/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 1.0991 - val_accuracy: 0.6778 Epoch 27/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 1.1146 - val_accuracy: 0.6833 Epoch 28/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.1304 - val_accuracy: 0.6833 Epoch 29/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 1.1334 - val_accuracy: 0.6833 Epoch 30/30 53/53 [==============================] - 2s 34ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 1.1427 - val_accuracy: 0.6778
test_ds = preprocess_input(X_test1)
fcl_loss, fcl_accuracy = cnn_VGG16_model.evaluate(test_ds, y_test, verbose=1)
print('Test loss:', fcl_loss)
print('Test accuracy:', fcl_accuracy)
## The Test accuracy score if 64% but the training accurayc is 100 , this model is overfit
12/12 [==============================] - 0s 27ms/step - loss: 1.1747 - accuracy: 0.6333 Test loss: 1.1746717691421509 Test accuracy: 0.6333333253860474
resultsDf1 = pd.concat([resultDF, createResultDf("CNN With VGG16",history.history['accuracy'][-1],fcl_accuracy)])
from sklearn.metrics import recall_score, confusion_matrix, precision_score, f1_score, accuracy_score, roc_auc_score,classification_report
from sklearn.metrics import classification_report
Y_truepred = np.argmax(y_test,axis = 1)
Y_testPred = cnn.predict(test_ds)
# Convert predictions classes to one hot vectors
Y_pred_classes = np.argmax(Y_pred,axis = 1)
reportData = classification_report(Y_truepred, Y_pred_classes,output_dict=True)
for data in reportData:
if(data == '-1' or data == '1'):
if(type(reportData[data]) is dict):
for subData in reportData[data]:
resultsDf1[data+"_"+subData] = reportData[data][subData]
resultsDf1
| Method | accuracy | Test Score | 1_precision | 1_recall | 1_f1-score | 1_support | |
|---|---|---|---|---|---|---|---|
| 0 | CNN | 0.552381 | 0.472222 | 0.348214 | 0.367925 | 0.357798 | 106 |
| 0 | CNN With VGG16 | 1.000000 | 0.633333 | 0.348214 | 0.367925 | 0.357798 | 106 |
CNN with ResNet50
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras import layers, models
resnet_base_model = ResNet50(include_top=False, weights='imagenet', input_shape=X_train[0].shape)
#model = VGG16(include_top=False, weights='imagenet', input_shape=(256,256,3))
train_ds = preprocess_input(X_train1)
train_val_df = preprocess_input(X_val1)
flatten_layer = layers.Flatten()
dense_layer_1 = layers.Dense(50, activation='relu')
dense_layer_2 = layers.Dense(32, activation='relu')
prediction_layer = layers.Dense(3, activation='softmax')
cnn_resnet_model = models.Sequential([
resnet_base_model,
flatten_layer,
dense_layer_1,
dense_layer_2,
prediction_layer
])
from tensorflow.keras.callbacks import EarlyStopping
cnn_resnet_model.compile(
optimizer='Adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'],
)
history =cnn_resnet_model.fit(train_ds, y_train, epochs=30, validation_data=(train_val_df,y_val))
Epoch 1/30 53/53 [==============================] - 11s 100ms/step - loss: 0.6364 - accuracy: 0.5565 - val_loss: 150.1105 - val_accuracy: 0.3389 Epoch 2/30 53/53 [==============================] - 4s 81ms/step - loss: 0.4891 - accuracy: 0.6417 - val_loss: 2.1864 - val_accuracy: 0.3750 Epoch 3/30 53/53 [==============================] - 4s 81ms/step - loss: 0.4695 - accuracy: 0.6440 - val_loss: 0.8023 - val_accuracy: 0.5417 Epoch 4/30 53/53 [==============================] - 4s 81ms/step - loss: 0.4501 - accuracy: 0.6804 - val_loss: 0.7970 - val_accuracy: 0.5528 Epoch 5/30 53/53 [==============================] - 4s 81ms/step - loss: 0.4037 - accuracy: 0.7048 - val_loss: 0.7922 - val_accuracy: 0.5833 Epoch 6/30 53/53 [==============================] - 4s 81ms/step - loss: 0.3642 - accuracy: 0.7512 - val_loss: 0.9936 - val_accuracy: 0.5111 Epoch 7/30 53/53 [==============================] - 4s 81ms/step - loss: 0.3600 - accuracy: 0.7637 - val_loss: 0.6201 - val_accuracy: 0.5333 Epoch 8/30 53/53 [==============================] - 4s 81ms/step - loss: 0.3686 - accuracy: 0.7518 - val_loss: 0.8325 - val_accuracy: 0.5250 Epoch 9/30 53/53 [==============================] - 4s 81ms/step - loss: 0.2988 - accuracy: 0.8042 - val_loss: 0.6359 - val_accuracy: 0.5639 Epoch 10/30 53/53 [==============================] - 4s 81ms/step - loss: 0.2741 - accuracy: 0.8286 - val_loss: 0.5571 - val_accuracy: 0.6056 Epoch 11/30 53/53 [==============================] - 4s 81ms/step - loss: 0.2202 - accuracy: 0.8667 - val_loss: 1.3907 - val_accuracy: 0.6000 Epoch 12/30 53/53 [==============================] - 4s 81ms/step - loss: 0.1826 - accuracy: 0.8940 - val_loss: 0.8978 - val_accuracy: 0.5778 Epoch 13/30 53/53 [==============================] - 4s 81ms/step - loss: 0.1836 - accuracy: 0.8923 - val_loss: 0.8572 - val_accuracy: 0.5917 Epoch 14/30 53/53 [==============================] - 4s 81ms/step - loss: 0.2096 - accuracy: 0.8827 - val_loss: 0.9837 - val_accuracy: 0.5500 Epoch 15/30 53/53 [==============================] - 4s 81ms/step - loss: 0.1399 - accuracy: 0.9292 - val_loss: 1.5493 - val_accuracy: 0.5694 Epoch 16/30 53/53 [==============================] - 4s 81ms/step - loss: 0.0796 - accuracy: 0.9548 - val_loss: 1.1490 - val_accuracy: 0.6694 Epoch 17/30 53/53 [==============================] - 4s 81ms/step - loss: 0.1310 - accuracy: 0.9280 - val_loss: 0.9451 - val_accuracy: 0.5944 Epoch 18/30 53/53 [==============================] - 4s 81ms/step - loss: 0.0701 - accuracy: 0.9655 - val_loss: 1.2134 - val_accuracy: 0.5306 Epoch 19/30 53/53 [==============================] - 4s 81ms/step - loss: 0.0780 - accuracy: 0.9607 - val_loss: 0.9952 - val_accuracy: 0.6111 Epoch 20/30 53/53 [==============================] - 4s 81ms/step - loss: 0.0635 - accuracy: 0.9631 - val_loss: 0.9911 - val_accuracy: 0.6250 Epoch 21/30 53/53 [==============================] - 4s 81ms/step - loss: 0.0493 - accuracy: 0.9762 - val_loss: 1.0681 - val_accuracy: 0.6222 Epoch 22/30 53/53 [==============================] - 4s 81ms/step - loss: 0.0399 - accuracy: 0.9792 - val_loss: 1.1820 - val_accuracy: 0.6639 Epoch 23/30 53/53 [==============================] - 4s 81ms/step - loss: 0.0493 - accuracy: 0.9762 - val_loss: 1.1898 - val_accuracy: 0.6583 Epoch 24/30 53/53 [==============================] - 4s 81ms/step - loss: 0.0434 - accuracy: 0.9780 - val_loss: 1.2652 - val_accuracy: 0.5917 Epoch 25/30 53/53 [==============================] - 4s 81ms/step - loss: 0.1195 - accuracy: 0.9375 - val_loss: 1.1965 - val_accuracy: 0.5972 Epoch 26/30 53/53 [==============================] - 4s 81ms/step - loss: 0.0632 - accuracy: 0.9631 - val_loss: 1.0476 - val_accuracy: 0.6083 Epoch 27/30 53/53 [==============================] - 4s 81ms/step - loss: 0.0654 - accuracy: 0.9661 - val_loss: 1.1409 - val_accuracy: 0.6306 Epoch 28/30 53/53 [==============================] - 4s 81ms/step - loss: 0.0268 - accuracy: 0.9863 - val_loss: 1.0929 - val_accuracy: 0.6583 Epoch 29/30 53/53 [==============================] - 4s 82ms/step - loss: 0.0383 - accuracy: 0.9821 - val_loss: 1.7980 - val_accuracy: 0.5917 Epoch 30/30 53/53 [==============================] - 4s 81ms/step - loss: 0.0758 - accuracy: 0.9637 - val_loss: 1.2048 - val_accuracy: 0.6500
fcl_loss, fcl_accuracy = cnn_resnet_model.evaluate(X_test, y_test, verbose=1)
print('Test loss:', fcl_loss)
print('Test accuracy:', fcl_accuracy)
## Thw accuracy of test score is 58% abut training accuracy is 99% again it is an overfit model
12/12 [==============================] - 1s 24ms/step - loss: 1.0424 - accuracy: 0.5139 Test loss: 1.042396068572998 Test accuracy: 0.5138888955116272
history.history['accuracy'][-1]
0.963690459728241
resultsDf2 = pd.concat([resultsDf1, createResultDf("CNN With ResNet50",history.history['accuracy'][-1],fcl_accuracy)])
from sklearn.metrics import recall_score, confusion_matrix, precision_score, f1_score, accuracy_score, roc_auc_score,classification_report
from sklearn.metrics import classification_report
Y_truepred = np.argmax(y_test,axis = 1)
Y_testPred = cnn.predict(X_test)
# Convert predictions classes to one hot vectors
Y_pred_classes = np.argmax(Y_pred,axis = 1)
reportData = classification_report(Y_truepred, Y_pred_classes,output_dict=True)
for data in reportData:
if(data == '-1' or data == '1'):
if(type(reportData[data]) is dict):
for subData in reportData[data]:
resultsDf2[data+"_"+subData] = reportData[data][subData]
from sklearn.metrics import confusion_matrix
import itertools
plt.subplots(figsize=(22,7)) #set the size of the plot
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
# Predict the values from the validation dataset
Y_pred = cnn_resnet_model.predict(test_ds)
# Convert predictions classes to one hot vectors
Y_pred_classes = np.argmax(Y_pred,axis = 1)
# Convert validation observations to one hot vectors
Y_true = np.argmax(y_test,axis = 1)
# compute the confusion matrix
confusion_mtx = confusion_matrix(Y_true, Y_pred_classes)
# plot the confusion matrix
plot_confusion_matrix(confusion_mtx, classes = range(3))
resultsDf2.head()
## with transfer learning we have the issue of overfitting, it does well in the training data set but not well in test
## Without TRansfer learning , the accuracy is not high but the model is general
## We will need to pre process tha image further and take more sample to conclude
| Method | accuracy | Test Score | 1_precision | 1_recall | 1_f1-score | 1_support | |
|---|---|---|---|---|---|---|---|
| 0 | CNN | 0.552381 | 0.472222 | 0.348214 | 0.367925 | 0.357798 | 106 |
| 0 | CNN With VGG16 | 1.000000 | 0.633333 | 0.348214 | 0.367925 | 0.357798 | 106 |
| 0 | CNN With ResNet50 | 0.963690 | 0.513889 | 0.348214 | 0.367925 | 0.357798 | 106 |
labels = pd.read_csv(Base_path + "stage_2_train_labels.csv")
## Getting the first 5000 data set for traingin and the next 5000 for validations
train_CombinedData = labels[0:5000]
validate_CombinedData = labels[5000:10000]
train_CombinedData.fillna(0, inplace=True)
validate_CombinedData.fillna(0, inplace=True)
## Checking training data distuburtion , they have almos the same target distubution
train_CombinedData.Target.value_counts()
0 2633 1 2367 Name: Target, dtype: int64
from tensorflow.keras.applications.mobilenet import preprocess_input
#
BATCH_SIZE = 10
## Image Size to be scaled
IMAGE_SIZE = 224
## Actual Image size
IMG_WIDTH = 1024
IMG_HEIGHT = 1024
class TrainGenerator(Sequence):
def __init__(self, _labels):
self.pids = _labels["patientId"].to_numpy()
self.coords = _labels[["x", "y", "width", "height"]].to_numpy()
self.coords = self.coords * IMAGE_SIZE / IMG_WIDTH
def __len__(self):
return math.ceil(len(self.coords) / BATCH_SIZE)
"""
The contrast of an image is enhanced when various shades in the image becomes more distinct.
We can do so by darkening the shades of the darker pixels and vice versa.
This is equivalent to widening the range of pixel intensities. To have a good contrast,
the following histogram characteristics are desirable:
1) the pixel intensities are uniformly distributed across the full range of values (each intensity value is equally probable), and
2) the cumulative histogram is increasing linearly across the full intensity range.
Histogram equalization modifies the distribution of pixel intensities to achieve these characteristics.
"""
def __doHistogramEqualization(self,img):
# Pre processing Histogram equalization
histogram_array = np.bincount(img.flatten(), minlength=256)
#normalize
num_pixels = np.sum(histogram_array)
histogram_array = histogram_array/num_pixels
#normalized cumulative histogram
chistogram_array = np.cumsum(histogram_array)
"""
STEP 2: Pixel mapping lookup table
"""
transform_map = np.floor(255 * chistogram_array).astype(np.uint8)
"""
STEP 3: Transformation
"""
img_list = list(img.flatten())
# transform pixel values to equalize
eq_img_list = [transform_map[p] for p in img_list]
# reshape and write back into img_array
img = np.reshape(np.asarray(eq_img_list), img.shape)
return img
def __getitem__(self, idx): # Get a batch
batch_coords = self.coords[idx * BATCH_SIZE:(idx + 1) * BATCH_SIZE] # Image coords
batch_pids = self.pids[idx * BATCH_SIZE:(idx + 1) * BATCH_SIZE] # Image pids
batch_images = np.zeros((len(batch_pids), IMAGE_SIZE, IMAGE_SIZE, 3), dtype=np.float32)
batch_masks = np.zeros((len(batch_pids), IMAGE_SIZE, IMAGE_SIZE))
for _indx, _pid in enumerate(batch_pids):
_path = Base_path + 'stage_2_train_images/'+'{}.dcm'.format(_pid)
_imgData = dcm.read_file(_path)
img = _imgData.pixel_array
# img = np.stack((img,)*3, axis=-1) # Expand grayscale image to contain 3 channels
# Resize image
resized_img = cv2.resize(img,(IMAGE_SIZE,IMAGE_SIZE), interpolation = cv2.INTER_AREA)
resized_img = self.__doHistogramEqualization(resized_img)
batch_images[_indx][:,:,0] = preprocess_input(np.array(resized_img[:,:], dtype=np.float32))
batch_images[_indx][:,:,1] = preprocess_input(np.array(resized_img[:,:], dtype=np.float32))
batch_images[_indx][:,:,2] = preprocess_input(np.array(resized_img[:,:], dtype=np.float32))
x = int(batch_coords[_indx, 0])
y = int(batch_coords[_indx, 1])
width = int(batch_coords[_indx, 2])
height = int(batch_coords[_indx, 3])
batch_masks[_indx][y:y+height, x:x+width] = 1
return batch_images, batch_masks
trainUNetDataGen = TrainGenerator( train_CombinedData)
validateUNetDataGen = TrainGenerator( validate_CombinedData)
# To show image with mask
def showMaskedImage(_imageSet, _maskSet, _index) :
maskImage = _imageSet[_index]
maskImage[:,:,0] = _maskSet[_index] * _imageSet[_index][:,:,0]
maskImage[:,:,1] = _maskSet[_index] * _imageSet[_index][:,:,1]
maskImage[:,:,2] = _maskSet[_index] * _imageSet[_index][:,:,2]
plt.imshow(maskImage[:,:,0])
## One of the pre processed image from custom train generotr
imageSet0 = trainUNetDataGen[1][0][1]
plt.imshow(imageSet0)
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
<matplotlib.image.AxesImage at 0x7f8705c04610>
## Masks for the same
imageSet0 = trainUNetDataGen[2][0]
maskSet0 = trainUNetDataGen[2][1]
showMaskedImage(imageSet0, maskSet0, 5)
LR = 1e-4
import os
import numpy as np
import pandas as pd
import cv2
from glob import glob
import tensorflow as tf
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Conv2D, Activation, BatchNormalization ,Conv2DTranspose
from tensorflow.keras.layers import UpSampling2D, Input, Concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.applications import MobileNet , VGG19
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
from tensorflow.keras.metrics import Recall, Precision
from tensorflow.keras import backend as K
from PIL import Image
from numpy import asarray
from tensorflow.keras.applications.mobilenet import MobileNet
from tensorflow.keras.layers import Concatenate, UpSampling2D, Conv2D, Reshape, GlobalAveragePooling2D
UNET USING MobileNet
ALPHA = 1.0
def create_UNetModelUsingMobileNet(trainable=True):
"""Function to create UNet architecture with MobileNet.
Arguments:
trainable -- Flag to make layers trainable. Default value is 'True'.
"""
# Get all layers with 'imagenet' weights
model = MobileNet(input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3), include_top=False, alpha=ALPHA, weights="imagenet")
# Top layer is last layer of the model
# Make all layers trainable
for layer in model.layers:
layer.trainable = trainable
# Add all the UNET layers here
convLayer_112by112 = model.get_layer("conv_pw_1_relu").output
convLayer_56by56 = model.get_layer("conv_pw_3_relu").output
convLayer_28by28 = model.get_layer("conv_pw_5_relu").output
convLayer_14by14 = model.get_layer("conv_pw_11_relu").output
convLayer_7by7 = model.get_layer("conv_pw_13_relu").output
# The last layer of mobilenet model is of dimensions (7x7x1024)
# Start upsampling from 7x7 to 14x14 ...up to 224x224 to form UNET
# concatinate with the original image layer of the same size from MobileNet
x = Concatenate()([UpSampling2D()(convLayer_7by7), convLayer_14by14])
x = Concatenate()([UpSampling2D()(x), convLayer_28by28])
x = Concatenate()([UpSampling2D()(x), convLayer_56by56])
x = Concatenate()([UpSampling2D()(x), convLayer_112by112])
x = UpSampling2D(name="unet_last")(x) # upsample to 224x224
# Add classification layer
x = Conv2D(1, kernel_size=1, activation="sigmoid", name="masks")(x)
x = Reshape((IMAGE_SIZE, IMAGE_SIZE))(x)
return Model(inputs=model.input, outputs=x)
## Build a model
input_shape = (IMAGE_SIZE,IMAGE_SIZE,3)
model = create_UNetModelUsingMobileNet(input_shape)
model.summary()
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_10 (InputLayer) [(None, 224, 224, 3 0 []
)]
conv1 (Conv2D) (None, 112, 112, 32 864 ['input_10[0][0]']
)
conv1_bn (BatchNormalization) (None, 112, 112, 32 128 ['conv1[0][0]']
)
conv1_relu (ReLU) (None, 112, 112, 32 0 ['conv1_bn[0][0]']
)
conv_dw_1 (DepthwiseConv2D) (None, 112, 112, 32 288 ['conv1_relu[0][0]']
)
conv_dw_1_bn (BatchNormalizati (None, 112, 112, 32 128 ['conv_dw_1[0][0]']
on) )
conv_dw_1_relu (ReLU) (None, 112, 112, 32 0 ['conv_dw_1_bn[0][0]']
)
conv_pw_1 (Conv2D) (None, 112, 112, 64 2048 ['conv_dw_1_relu[0][0]']
)
conv_pw_1_bn (BatchNormalizati (None, 112, 112, 64 256 ['conv_pw_1[0][0]']
on) )
conv_pw_1_relu (ReLU) (None, 112, 112, 64 0 ['conv_pw_1_bn[0][0]']
)
conv_pad_2 (ZeroPadding2D) (None, 113, 113, 64 0 ['conv_pw_1_relu[0][0]']
)
conv_dw_2 (DepthwiseConv2D) (None, 56, 56, 64) 576 ['conv_pad_2[0][0]']
conv_dw_2_bn (BatchNormalizati (None, 56, 56, 64) 256 ['conv_dw_2[0][0]']
on)
conv_dw_2_relu (ReLU) (None, 56, 56, 64) 0 ['conv_dw_2_bn[0][0]']
conv_pw_2 (Conv2D) (None, 56, 56, 128) 8192 ['conv_dw_2_relu[0][0]']
conv_pw_2_bn (BatchNormalizati (None, 56, 56, 128) 512 ['conv_pw_2[0][0]']
on)
conv_pw_2_relu (ReLU) (None, 56, 56, 128) 0 ['conv_pw_2_bn[0][0]']
conv_dw_3 (DepthwiseConv2D) (None, 56, 56, 128) 1152 ['conv_pw_2_relu[0][0]']
conv_dw_3_bn (BatchNormalizati (None, 56, 56, 128) 512 ['conv_dw_3[0][0]']
on)
conv_dw_3_relu (ReLU) (None, 56, 56, 128) 0 ['conv_dw_3_bn[0][0]']
conv_pw_3 (Conv2D) (None, 56, 56, 128) 16384 ['conv_dw_3_relu[0][0]']
conv_pw_3_bn (BatchNormalizati (None, 56, 56, 128) 512 ['conv_pw_3[0][0]']
on)
conv_pw_3_relu (ReLU) (None, 56, 56, 128) 0 ['conv_pw_3_bn[0][0]']
conv_pad_4 (ZeroPadding2D) (None, 57, 57, 128) 0 ['conv_pw_3_relu[0][0]']
conv_dw_4 (DepthwiseConv2D) (None, 28, 28, 128) 1152 ['conv_pad_4[0][0]']
conv_dw_4_bn (BatchNormalizati (None, 28, 28, 128) 512 ['conv_dw_4[0][0]']
on)
conv_dw_4_relu (ReLU) (None, 28, 28, 128) 0 ['conv_dw_4_bn[0][0]']
conv_pw_4 (Conv2D) (None, 28, 28, 256) 32768 ['conv_dw_4_relu[0][0]']
conv_pw_4_bn (BatchNormalizati (None, 28, 28, 256) 1024 ['conv_pw_4[0][0]']
on)
conv_pw_4_relu (ReLU) (None, 28, 28, 256) 0 ['conv_pw_4_bn[0][0]']
conv_dw_5 (DepthwiseConv2D) (None, 28, 28, 256) 2304 ['conv_pw_4_relu[0][0]']
conv_dw_5_bn (BatchNormalizati (None, 28, 28, 256) 1024 ['conv_dw_5[0][0]']
on)
conv_dw_5_relu (ReLU) (None, 28, 28, 256) 0 ['conv_dw_5_bn[0][0]']
conv_pw_5 (Conv2D) (None, 28, 28, 256) 65536 ['conv_dw_5_relu[0][0]']
conv_pw_5_bn (BatchNormalizati (None, 28, 28, 256) 1024 ['conv_pw_5[0][0]']
on)
conv_pw_5_relu (ReLU) (None, 28, 28, 256) 0 ['conv_pw_5_bn[0][0]']
conv_pad_6 (ZeroPadding2D) (None, 29, 29, 256) 0 ['conv_pw_5_relu[0][0]']
conv_dw_6 (DepthwiseConv2D) (None, 14, 14, 256) 2304 ['conv_pad_6[0][0]']
conv_dw_6_bn (BatchNormalizati (None, 14, 14, 256) 1024 ['conv_dw_6[0][0]']
on)
conv_dw_6_relu (ReLU) (None, 14, 14, 256) 0 ['conv_dw_6_bn[0][0]']
conv_pw_6 (Conv2D) (None, 14, 14, 512) 131072 ['conv_dw_6_relu[0][0]']
conv_pw_6_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_pw_6[0][0]']
on)
conv_pw_6_relu (ReLU) (None, 14, 14, 512) 0 ['conv_pw_6_bn[0][0]']
conv_dw_7 (DepthwiseConv2D) (None, 14, 14, 512) 4608 ['conv_pw_6_relu[0][0]']
conv_dw_7_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_dw_7[0][0]']
on)
conv_dw_7_relu (ReLU) (None, 14, 14, 512) 0 ['conv_dw_7_bn[0][0]']
conv_pw_7 (Conv2D) (None, 14, 14, 512) 262144 ['conv_dw_7_relu[0][0]']
conv_pw_7_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_pw_7[0][0]']
on)
conv_pw_7_relu (ReLU) (None, 14, 14, 512) 0 ['conv_pw_7_bn[0][0]']
conv_dw_8 (DepthwiseConv2D) (None, 14, 14, 512) 4608 ['conv_pw_7_relu[0][0]']
conv_dw_8_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_dw_8[0][0]']
on)
conv_dw_8_relu (ReLU) (None, 14, 14, 512) 0 ['conv_dw_8_bn[0][0]']
conv_pw_8 (Conv2D) (None, 14, 14, 512) 262144 ['conv_dw_8_relu[0][0]']
conv_pw_8_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_pw_8[0][0]']
on)
conv_pw_8_relu (ReLU) (None, 14, 14, 512) 0 ['conv_pw_8_bn[0][0]']
conv_dw_9 (DepthwiseConv2D) (None, 14, 14, 512) 4608 ['conv_pw_8_relu[0][0]']
conv_dw_9_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_dw_9[0][0]']
on)
conv_dw_9_relu (ReLU) (None, 14, 14, 512) 0 ['conv_dw_9_bn[0][0]']
conv_pw_9 (Conv2D) (None, 14, 14, 512) 262144 ['conv_dw_9_relu[0][0]']
conv_pw_9_bn (BatchNormalizati (None, 14, 14, 512) 2048 ['conv_pw_9[0][0]']
on)
conv_pw_9_relu (ReLU) (None, 14, 14, 512) 0 ['conv_pw_9_bn[0][0]']
conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512) 4608 ['conv_pw_9_relu[0][0]']
conv_dw_10_bn (BatchNormalizat (None, 14, 14, 512) 2048 ['conv_dw_10[0][0]']
ion)
conv_dw_10_relu (ReLU) (None, 14, 14, 512) 0 ['conv_dw_10_bn[0][0]']
conv_pw_10 (Conv2D) (None, 14, 14, 512) 262144 ['conv_dw_10_relu[0][0]']
conv_pw_10_bn (BatchNormalizat (None, 14, 14, 512) 2048 ['conv_pw_10[0][0]']
ion)
conv_pw_10_relu (ReLU) (None, 14, 14, 512) 0 ['conv_pw_10_bn[0][0]']
conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512) 4608 ['conv_pw_10_relu[0][0]']
conv_dw_11_bn (BatchNormalizat (None, 14, 14, 512) 2048 ['conv_dw_11[0][0]']
ion)
conv_dw_11_relu (ReLU) (None, 14, 14, 512) 0 ['conv_dw_11_bn[0][0]']
conv_pw_11 (Conv2D) (None, 14, 14, 512) 262144 ['conv_dw_11_relu[0][0]']
conv_pw_11_bn (BatchNormalizat (None, 14, 14, 512) 2048 ['conv_pw_11[0][0]']
ion)
conv_pw_11_relu (ReLU) (None, 14, 14, 512) 0 ['conv_pw_11_bn[0][0]']
conv_pad_12 (ZeroPadding2D) (None, 15, 15, 512) 0 ['conv_pw_11_relu[0][0]']
conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512) 4608 ['conv_pad_12[0][0]']
conv_dw_12_bn (BatchNormalizat (None, 7, 7, 512) 2048 ['conv_dw_12[0][0]']
ion)
conv_dw_12_relu (ReLU) (None, 7, 7, 512) 0 ['conv_dw_12_bn[0][0]']
conv_pw_12 (Conv2D) (None, 7, 7, 1024) 524288 ['conv_dw_12_relu[0][0]']
conv_pw_12_bn (BatchNormalizat (None, 7, 7, 1024) 4096 ['conv_pw_12[0][0]']
ion)
conv_pw_12_relu (ReLU) (None, 7, 7, 1024) 0 ['conv_pw_12_bn[0][0]']
conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024) 9216 ['conv_pw_12_relu[0][0]']
conv_dw_13_bn (BatchNormalizat (None, 7, 7, 1024) 4096 ['conv_dw_13[0][0]']
ion)
conv_dw_13_relu (ReLU) (None, 7, 7, 1024) 0 ['conv_dw_13_bn[0][0]']
conv_pw_13 (Conv2D) (None, 7, 7, 1024) 1048576 ['conv_dw_13_relu[0][0]']
conv_pw_13_bn (BatchNormalizat (None, 7, 7, 1024) 4096 ['conv_pw_13[0][0]']
ion)
conv_pw_13_relu (ReLU) (None, 7, 7, 1024) 0 ['conv_pw_13_bn[0][0]']
up_sampling2d_8 (UpSampling2D) (None, 14, 14, 1024 0 ['conv_pw_13_relu[0][0]']
)
concatenate_8 (Concatenate) (None, 14, 14, 1536 0 ['up_sampling2d_8[0][0]',
) 'conv_pw_11_relu[0][0]']
up_sampling2d_9 (UpSampling2D) (None, 28, 28, 1536 0 ['concatenate_8[0][0]']
)
concatenate_9 (Concatenate) (None, 28, 28, 1792 0 ['up_sampling2d_9[0][0]',
) 'conv_pw_5_relu[0][0]']
up_sampling2d_10 (UpSampling2D (None, 56, 56, 1792 0 ['concatenate_9[0][0]']
) )
concatenate_10 (Concatenate) (None, 56, 56, 1920 0 ['up_sampling2d_10[0][0]',
) 'conv_pw_3_relu[0][0]']
up_sampling2d_11 (UpSampling2D (None, 112, 112, 19 0 ['concatenate_10[0][0]']
) 20)
concatenate_11 (Concatenate) (None, 112, 112, 19 0 ['up_sampling2d_11[0][0]',
84) 'conv_pw_1_relu[0][0]']
unet_last (UpSampling2D) (None, 224, 224, 19 0 ['concatenate_11[0][0]']
84)
masks (Conv2D) (None, 224, 224, 1) 1985 ['unet_last[0][0]']
reshape_2 (Reshape) (None, 224, 224) 0 ['masks[0][0]']
==================================================================================================
Total params: 3,230,849
Trainable params: 3,208,961
Non-trainable params: 21,888
__________________________________________________________________________________________________
#dice_coef 2 * the Area of Overlap divided by the total number of pixels in both images
def dice_coef(y_true, y_pred):
y_true = tf.keras.layers.Flatten()(y_true)
y_pred = tf.keras.layers.Flatten()(y_pred)
intersection = tf.reduce_sum(y_true * y_pred)
return (2. * intersection + tf.keras.backend.epsilon()) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) + tf.keras.backend.epsilon())
## Loss is 1 - the coefficent of two images
def dice_loss(y_true, y_pred):
return 1.0 - dice_coef(y_true, y_pred)
## Call Backs for ealy stopping and reduce learning rate
## Reduce the learning rate when teh validation loss is same for 4 epocs
callbacks = [
ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=4),
EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=False)
]
## Optimiser , metrics and loss for the model
opt = tf.keras.optimizers.Nadam(LR)
metrics = [dice_coef, Recall(), Precision()]
model.compile(loss=dice_loss, optimizer=opt, metrics=metrics)
## Running the model
train_steps = len(trainUNetDataGen)//BATCH_SIZE
valid_steps = len(validateUNetDataGen)//BATCH_SIZE
if len(trainUNetDataGen) % BATCH_SIZE != 0:
train_steps += 1
if len(validateUNetDataGen) % BATCH_SIZE != 0:
valid_steps += 1
history = model.fit(trainUNetDataGen,
epochs=16,
steps_per_epoch=train_steps,
validation_data=validateUNetDataGen,
callbacks=callbacks,
use_multiprocessing=True,
workers=4,
validation_steps=valid_steps,
shuffle=True)
Epoch 1/16 50/50 [==============================] - 36s 560ms/step - loss: 0.8460 - dice_coef: 0.1540 - recall_2: 0.7787 - precision_2: 0.0814 - val_loss: 0.8898 - val_dice_coef: 0.1102 - val_recall_2: 0.8016 - val_precision_2: 0.0724 - lr: 1.0000e-04 Epoch 2/16 50/50 [==============================] - 24s 473ms/step - loss: 0.7165 - dice_coef: 0.2835 - recall_2: 0.5978 - precision_2: 0.2112 - val_loss: 0.8286 - val_dice_coef: 0.1714 - val_recall_2: 0.5843 - val_precision_2: 0.1484 - lr: 1.0000e-04 Epoch 3/16 50/50 [==============================] - 28s 540ms/step - loss: 0.6735 - dice_coef: 0.3265 - recall_2: 0.5143 - precision_2: 0.2821 - val_loss: 0.8328 - val_dice_coef: 0.1672 - val_recall_2: 0.5532 - val_precision_2: 0.1394 - lr: 1.0000e-04 Epoch 4/16 50/50 [==============================] - 28s 541ms/step - loss: 0.6630 - dice_coef: 0.3370 - recall_2: 0.4949 - precision_2: 0.2827 - val_loss: 0.8329 - val_dice_coef: 0.1671 - val_recall_2: 0.6812 - val_precision_2: 0.1291 - lr: 1.0000e-04 Epoch 5/16 50/50 [==============================] - 28s 540ms/step - loss: 0.6275 - dice_coef: 0.3725 - recall_2: 0.5233 - precision_2: 0.3317 - val_loss: 0.8322 - val_dice_coef: 0.1678 - val_recall_2: 0.6975 - val_precision_2: 0.1225 - lr: 1.0000e-04 Epoch 6/16 50/50 [==============================] - 24s 473ms/step - loss: 0.6321 - dice_coef: 0.3679 - recall_2: 0.5092 - precision_2: 0.3292 - val_loss: 0.8049 - val_dice_coef: 0.1951 - val_recall_2: 0.5963 - val_precision_2: 0.1667 - lr: 1.0000e-04 Epoch 7/16 50/50 [==============================] - 28s 541ms/step - loss: 0.6259 - dice_coef: 0.3741 - recall_2: 0.5037 - precision_2: 0.3335 - val_loss: 0.8067 - val_dice_coef: 0.1933 - val_recall_2: 0.5090 - val_precision_2: 0.1899 - lr: 1.0000e-04 Epoch 8/16 50/50 [==============================] - 24s 475ms/step - loss: 0.6322 - dice_coef: 0.3678 - recall_2: 0.5081 - precision_2: 0.3169 - val_loss: 0.8219 - val_dice_coef: 0.1781 - val_recall_2: 0.6916 - val_precision_2: 0.1349 - lr: 1.0000e-04 Epoch 9/16 50/50 [==============================] - 25s 475ms/step - loss: 0.6300 - dice_coef: 0.3700 - recall_2: 0.5226 - precision_2: 0.3136 - val_loss: 0.8083 - val_dice_coef: 0.1917 - val_recall_2: 0.6666 - val_precision_2: 0.1480 - lr: 1.0000e-04 Epoch 10/16 50/50 [==============================] - 24s 475ms/step - loss: 0.6324 - dice_coef: 0.3676 - recall_2: 0.5176 - precision_2: 0.3282 - val_loss: 0.8076 - val_dice_coef: 0.1924 - val_recall_2: 0.6776 - val_precision_2: 0.1530 - lr: 1.0000e-04 Epoch 11/16 50/50 [==============================] - 28s 540ms/step - loss: 0.6243 - dice_coef: 0.3757 - recall_2: 0.5433 - precision_2: 0.3213 - val_loss: 0.8036 - val_dice_coef: 0.1964 - val_recall_2: 0.6860 - val_precision_2: 0.1567 - lr: 1.0000e-05 Epoch 12/16 50/50 [==============================] - 24s 475ms/step - loss: 0.6245 - dice_coef: 0.3755 - recall_2: 0.5463 - precision_2: 0.3211 - val_loss: 0.7990 - val_dice_coef: 0.2010 - val_recall_2: 0.6632 - val_precision_2: 0.1636 - lr: 1.0000e-05 Epoch 13/16 50/50 [==============================] - 28s 540ms/step - loss: 0.6100 - dice_coef: 0.3900 - recall_2: 0.4913 - precision_2: 0.3606 - val_loss: 0.7977 - val_dice_coef: 0.2023 - val_recall_2: 0.6489 - val_precision_2: 0.1680 - lr: 1.0000e-05 Epoch 14/16 50/50 [==============================] - 24s 473ms/step - loss: 0.6254 - dice_coef: 0.3746 - recall_2: 0.5081 - precision_2: 0.3290 - val_loss: 0.7973 - val_dice_coef: 0.2027 - val_recall_2: 0.6437 - val_precision_2: 0.1718 - lr: 1.0000e-05 Epoch 15/16 50/50 [==============================] - 25s 475ms/step - loss: 0.5819 - dice_coef: 0.4181 - recall_2: 0.5132 - precision_2: 0.3781 - val_loss: 0.7980 - val_dice_coef: 0.2020 - val_recall_2: 0.6520 - val_precision_2: 0.1719 - lr: 1.0000e-05 Epoch 16/16 50/50 [==============================] - 28s 542ms/step - loss: 0.6025 - dice_coef: 0.3975 - recall_2: 0.5190 - precision_2: 0.3543 - val_loss: 0.7982 - val_dice_coef: 0.2018 - val_recall_2: 0.6377 - val_precision_2: 0.1747 - lr: 1.0000e-05
## PLot Matrix between training and validation data
plt.figure(figsize=(8, 5))
plt.grid(True)
plt.plot(history.history['dice_coef'], label='Train Dice-Coef', color = "green" );
plt.plot(history.history['val_dice_coef'], label='Val Dice-Coef', color = "yellow");
plt.plot(history.history['loss'], label='Train Loss', color = "red" );
plt.plot(history.history['val_loss'], label='Val Loss', color = "orange");
plt.title("Validation and Training - Loss and Dice Coefficient vs Epoch")
plt.xlabel("Epoch")
plt.legend();
val_dice-oefficient value is very low and pretty much a flat curve, indicating underfitting indicating model has not learnt sufficiently. It is steadily increasing, not sufficient training(more epochs needed).
We have used an image size of 224x224 as against the original size of 1024x1024. Using a higher resolution, could also improve training capacity
##Preparing test data , picked up random 100 images
test_CombinedData = labels[15000:15100]
test_CombinedData.fillna(0, inplace=True)
##Check target distrubution in test dataset, there are both the classes available with equal sdistrubution
test_CombinedData.Target.value_counts()
0 64 1 36 Name: Target, dtype: int64
## Setting the custom generator for test data
testUNetDataGen = TrainGenerator(test_CombinedData)
## evaluating the model
test_steps = (len(testUNetDataGen)//BATCH_SIZE)
if len(testUNetDataGen) % BATCH_SIZE != 0:
test_steps += 1
score = model.evaluate(testUNetDataGen)
## Model evaluation , dice coef = 50% m recall is good wiht 61% , precission is very low , of about 43.37%
10/10 [==============================] - 3s 250ms/step - loss: 0.5909 - dice_coef: 0.4091 - recall_2: 0.4821 - precision_2: 0.4052
print (score)
[0.5908868908882141, 0.40911316871643066, 0.48213866353034973, 0.4052044451236725]
## Precidt the test data that we have
pred_mask = model.predict(testUNetDataGen)
test_CombinedData = test_CombinedData.reset_index()
from sklearn.metrics import confusion_matrix
y_pred = []
y_True = []
imageList = []
predMaskTemp = []
IMAGE_HEIGHT = 224
IMAGE_WIDTH = 224
def getPredictions(test_CombinedData):
masks = np.zeros((int(test_CombinedData.shape[0]), IMAGE_HEIGHT, IMAGE_WIDTH))
for index, row in test_CombinedData.iterrows():
patientId = row.patientId
# print(patientId)
classlabel = row["Target"]
dcm_file = Base_path + 'stage_2_train_images/'+'{}.dcm'.format(patientId)
dcm_data = dcm.read_file(dcm_file)
img = dcm_data.pixel_array
resized_img = cv2.resize(img,(IMAGE_HEIGHT,IMAGE_WIDTH), interpolation = cv2.INTER_LINEAR)
predMaskTemp.append(pred_mask[index])
iou = (pred_mask[index] > 0.5) * 1.0
y_pred.append((1 in iou) * 1)
imageList.append(resized_img)
y_True.append(classlabel)
x_scale = IMAGE_HEIGHT / 1024
y_scale = IMAGE_WIDTH / 1024
if(classlabel == 1):
x = int(np.round(row['x'] * x_scale))
y = int(np.round(row['y'] * y_scale))
w = int(np.round(row['width'] * x_scale))
h = int(np.round(row['height'] * y_scale))
masks[index][y:y+h, x:x+w] = 1
tmpImages = np.array(imageList)
tmpMask = np.array(predMaskTemp)
originalMask = np.array(masks)
return (y_True,y_pred,tmpImages,tmpMask ,originalMask)
def print_confusion_matrix(y_true, y_pred):
'''Function to print confusion_matrix'''
# Get confusion matrix array
array = confusion_matrix(y_true, y_pred)
df_cm = pd.DataFrame(array, range(2), range(2))
print("Total samples = ", len(test_CombinedData))
# Plot heatmap and get sns heatmap values
sns.set(font_scale=1.4); # for label size
result = sns.heatmap(df_cm, annot=True, annot_kws={"size": 16}, fmt='g', cbar=False);
# Add labels to heatmap
labels = ['TN=','FP=','FN=','TP=']
i=0
for t in result.texts:
t.set_text(labels[i] + t.get_text())
i += 1
plt.xlabel("Predicted Values")
plt.ylabel('True Values')
plt.show()
return
## Create predictions map
y_true,y_pred ,imagelist , maskList , originalMask = getPredictions(test_CombinedData)
dcm_file = Base_path + 'stage_2_train_images/'+'{}.dcm'.format('9358d1c5-ba61-4150-a233-41138208a3f9')
dcm_data = dcm.read_file(dcm_file)
img = dcm_data.pixel_array
plt.imshow(imagelist[12])
<matplotlib.image.AxesImage at 0x7f86844674d0>
## Visualising the train and output data
##
fig = plt.figure(figsize=(15, 15))
a = fig.add_subplot(1, 4, 1)
imgplot = plt.imshow(imagelist[1])
a.set_title('Original Images ',fontsize=20)
a = fig.add_subplot(1, 4, 2)
imgplot = plt.imshow(imagelist[12])
a = fig.add_subplot(1, 4, 3)
imgplot = plt.imshow(imagelist[13])
a = fig.add_subplot(1, 4, 4)
imgplot = plt.imshow(imagelist[15])
fig = plt.figure(figsize=(15, 15))
a = fig.add_subplot(1, 4, 1)
imgplot = plt.imshow(originalMask[1])
a.set_title('Oringial Mask (Truth) ',fontsize=20)
a.set_xlabel('Pneumonia {}:'.format(y_true[1]), fontsize=20)
a = fig.add_subplot(1, 4, 2)
imgplot = plt.imshow(originalMask[12])
a.set_xlabel('Pneumonia {}:'.format(y_true[12]), fontsize=20)
a = fig.add_subplot(1, 4, 3)
imgplot = plt.imshow(originalMask[13])
a.set_xlabel('Pneumonia {}:'.format(y_true[13]), fontsize=20)
a = fig.add_subplot(1, 4, 4)
imgplot = plt.imshow(originalMask[15])
a.set_xlabel('Pneumonia {}:'.format(y_true[15]), fontsize=20)
fig = plt.figure(figsize=(15, 15))
a = fig.add_subplot(1, 4, 1)
a.set_title('Predicted Mask ',fontsize=20)
imgplot = plt.imshow(maskList[1])
a.set_xlabel('Pneumonia {}:'.format(y_pred[1]), fontsize=20)
a = fig.add_subplot(1, 4, 2)
imgplot = plt.imshow(maskList[12])
a.set_xlabel('Pneumonia {}:'.format(y_pred[12]), fontsize=20)
a = fig.add_subplot(1, 4, 3)
imgplot = plt.imshow(maskList[13])
a.set_xlabel('Pneumonia {}:'.format(y_pred[13]), fontsize=20)
a = fig.add_subplot(1, 4, 4)
imgplot = plt.imshow(maskList[15])
a.set_xlabel('Pneumonia {}:'.format(y_pred[15]), fontsize=20)
## we could see that the first one is mis classifed
## Second one is classified correctly , there are no masks
## Thrid is predcited correctly and the bouding box is also almost at the same position
## fourth is predicted correctly but there are two boudnign boxes, this culd be because there are duplicate patient id and we are picking only one mask to display inte Truth
Text(0.5, 0, 'Pneumonia 1:')
#Y_pred_new = np.select([Y_pred <= .3, Y_pred>.7], [np.zeros_like(Y_pred), np.ones_like(Y_pred)])
Y_pred_rounded = np.argmax(Y_pred, axis=1)
reportData = classification_report(Y_true, Y_pred_rounded)
print (reportData)
precision recall f1-score support
0 0.68 0.69 0.69 131
1 0.56 0.48 0.52 106
2 0.78 0.86 0.82 123
accuracy 0.69 360
macro avg 0.67 0.68 0.68 360
weighted avg 0.68 0.69 0.68 360
print_confusion_matrix(y_true,y_pred )
## There are two False Postive for Target 1
Total samples = 100
from sklearn.metrics import classification_report
reportData = classification_report(y_true,y_pred, output_dict = True)
print (reportData)
{'0': {'precision': 0.9803921568627451, 'recall': 0.78125, 'f1-score': 0.8695652173913042, 'support': 64}, '1': {'precision': 0.7142857142857143, 'recall': 0.9722222222222222, 'f1-score': 0.8235294117647058, 'support': 36}, 'accuracy': 0.85, 'macro avg': {'precision': 0.8473389355742297, 'recall': 0.8767361111111112, 'f1-score': 0.846547314578005, 'support': 100}, 'weighted avg': {'precision': 0.884593837535014, 'recall': 0.85, 'f1-score': 0.8529923273657287, 'support': 100}}
resultsDf3 = createResultDf("UNET",0.88,0.88)
for data in reportData:
if(data == '1'):
if(type(reportData[data]) is dict):
for subData in reportData[data]:
resultsDf3[data+"_"+subData] = reportData[data][subData]
resultsDf3
| Method | accuracy | Test Score | 1_precision | 1_recall | 1_f1-score | 1_support | |
|---|---|---|---|---|---|---|---|
| 0 | UNET | 0.88 | 0.88 | 0.714286 | 0.972222 | 0.823529 | 36 |
resultsDfFinal = pd.concat([resultsDf2,resultsDf3])
resultsDfFinal
| Method | accuracy | Test Score | 1_precision | 1_recall | 1_f1-score | 1_support | |
|---|---|---|---|---|---|---|---|
| 0 | CNN | 0.552381 | 0.472222 | 0.348214 | 0.367925 | 0.357798 | 106 |
| 0 | CNN With VGG16 | 1.000000 | 0.633333 | 0.348214 | 0.367925 | 0.357798 | 106 |
| 0 | CNN With ResNet50 | 0.963690 | 0.513889 | 0.348214 | 0.367925 | 0.357798 | 106 |
| 0 | UNET | 0.880000 | 0.880000 | 0.714286 | 0.972222 | 0.823529 | 36 |
resultsDfFinal.rename(columns={'1_precision': 'precision', '1_recall': 'recall','1_f1-score': 'f1-score'}, inplace=True)
resultsDfFinal.plot(x="Method", y=["Test Score","precision","recall","f1-score"], figsize=(20, 15))
plt.show()
1) We have done the EDA
2) We have done the pre-processing of the dicom images
3) We have built CNN model for classification without transfer learning and with Transfer learning
4) Currently with the limited sample data set that we have taken we have found the CNN model without transfer learning was the most general model even though the accuracy was not very high
5) We have built UNET model for regression using VGG16 Pre trained model
6) We can improve the dice loss by image augmentation , increasing the sample size and hyper tuning